eindhoven university of technology master dock & yard ...eindhoven university of technology...
TRANSCRIPT
Eindhoven University of Technology
MASTER
Dock & Yard management at KLM Cargoa truck sequencing study with multiple levels of location information availability
Blonk, M.
Award date:2017
Link to publication
DisclaimerThis document contains a student thesis (bachelor's or master's), as authored by a student at Eindhoven University of Technology. Studenttheses are made available in the TU/e repository upon obtaining the required degree. The grade received is not published on the documentas presented in the repository. The required complexity or quality of research of student theses may vary by program, and the requiredminimum study period may vary in duration.
General rightsCopyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright ownersand it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights.
• Users may download and print one copy of any publication from the public portal for the purpose of private study or research. • You may not further distribute the material or use it for any profit-making activity or commercial gain
August 2017
Schiphol
Dock & Yard Management at KLM Cargo
A truck sequencing study with multiple levels of location information availability
Master Thesis
by Maarten Blonk
student number: 0909632
Eindhoven University of Technology
Industrial Engineering and Innovation Sciences
Supervised by dr. L. P. Veelenturf Eindhoven University of Technology
B. Krol Air France – KLM
Co-reader prof. dr. A. W. Veenstra Eindhoven University of Technology
Third assessor prof. dr. T. van Woensel Eindhoven University of Technology
Page intentionally left blank
Keywords: Air cargo transportation, Air freight, Truck sequencing, Truck scheduling, job scheduling,
Truck handling, priority rules, discrete event simulation, Enterprise Dynamics, Air France, KLM, KLM
Cargo, Dock & Yard Management
Thesis details
MSc. Thesis
Title: Dock & Yard Management at KLM Cargo
Subtitle: A truck sequencing study with multiple levels of location information availability
Author
Name: ing. M. Blonk
Student number: 0909632
Master programme: Operations Management & Logistics
Contact: [email protected]
Graduation Committee
First supervisor: dr. L.P. Veelenturf
Assistant Professor, Industrial Engineering & Innovation Sciences (OPAC)
Second supervisor: prof. dr. A.W. Veenstra
Full Professor, Industrial Engineering & Innovation Sciences (OPAC)
Third reader: prof. dr. T. van Woensel
Full Professor, Industrial Engineering & Innovation Sciences (OPAC)
External supervisor: B. Krol
Project Manager Air France – KLM Cargo, Business & Process Improvement
Eindhoven University of Technology
August 14, 2017
Contents 1. Introduction ........................................................................................................................................... 1
Part I ............................................................................................................................................................ 4
2. Introduction to air freight transportation ............................................................................................... 5
3. Research outline .................................................................................................................................. 13
4. Literature review ................................................................................................................................. 20
Part II ......................................................................................................................................................... 24
5. Current process performance .............................................................................................................. 25
Part III ....................................................................................................................................................... 35
6. Experimental design and modelling .................................................................................................... 36
7. Results ................................................................................................................................................. 51
Part IV ....................................................................................................................................................... 64
8. Discussion and conclusions ................................................................................................................ 65
Bibliography ............................................................................................................................................... 68
Appendix A – Detailed air cargo process ..................................................................................................... 1
Appendix B – Truck arrival process ............................................................................................................. 5
Appendix C – Truck streams at the SPL Hub ............................................................................................... 6
Appendix D – Cause and effect diagram ...................................................................................................... 7
Appendix E – Calculation increase in FAP .................................................................................................. 8
Appendix F – Data description and availability ............................................................................................ 9
Appendix G – Statistical testing (z-test) ..................................................................................................... 12
Appendix H – Enterprise dynamics 8 ......................................................................................................... 13
Appendix I – Additional model assumptions .............................................................................................. 14
Appendix J – Model structure ..................................................................................................................... 16
Appendix K – Model parameters: current state .......................................................................................... 19
Appendix L – Model parameters: other scenarios ...................................................................................... 26
Appendix M – Model results: FAP per day ................................................................................................ 42
i
Preface
This month I proudly end an important, beautiful and fun period of my life. For four years I did my
bachelor’s on Aviation engineering, security and technology. The last three years of my student career I
broadened my educational horizon by pursuing my master’s degree at the TU/e, with a focus on Operations
Management and Logistics. This thesis document completes the final assignment of the master’s degree.
This thesis was written in cooperation with Air France – KLM Cargo and Eindhoven University of
Technology, its purpose being to enhance operational KPIs of KLM Cargo at Schiphol. Truck sequencing
strategies were applied to European trucking streams converging at Schiphol, in order to eventually get all
shipments processed on time to connecting flights.
I would like to thank everyone who contributed directly or indirectly to the writing process. In
particular, my university supervisor dr. Luuk Veelenturf, who guided me through the complete writing
process and challenging times, by giving feedback on a regular basis. Furthermore, I would especially like
to thank my external supervisor, Bart Krol, for giving me the opportunity to perform this study at KLM
Cargo, sharing all of his knowledge and supporting me wherever he could.
Next to my first supervisors, I would like to thank my second supervisor prof. dr. Albert Veenstra for
his feedback during two key moments in the process and prof. dr. Tom van Woensel for his feedback and
useful insights during feedback sessions.
In addition, I would like to thank all KLM Cargo employees who were involved during data gathering,
modelling, or were there to enlighten me with their knowledge on air cargo processes.
Finally, my thanks to go out to those around me including, my parents and other relatives, my girlfriend
and all of my friends for their continuous support and contribution throughout the process.
Thank you for taking the time to read this report, which will hopefully be a valuable asset to you. Enjoy!
Yours most sincerely,
Maarten Blonk
Schiphol, The Netherlands
August 14, 2017
ii
List of abbreviations
Abbreviation Explanation Page
ACN Air Cargo Netherlands 2
ARR Milestone: Cargo arrived from airport of destination 7
AWB Air Waybill 9
CET Chassis Exchange Terminal 21
DC Distribution Centre 23
DEP Milestone: Cargo departed from airport of origin 7
ED8 Enterprise Dynamics 8 36
EDD Earliest Due Date 17
EHS European Handling System 11
FAP Flown As Planned 1
FBL Freight Booked List 6
FCFS First Come First Served 17
FFM Milestone: Freight Forwarding Message 7
FIFO First In First Out 21
IATA International Air Transport Association 5
KLM Koninklijke Luchtvaart Maatschappij 1
KLM Cargo Air France - KLM Martinair Cargo 1
KPI Key Performance Indicator 1
MTD Moving Truck Device 10
M-ULD Mixed Unit Loading Device 9
NFD Milestone: Notified For Delivery 7
PCHS Pallet Container Handling System 14
RCF Milestone: Received from flight at airport of destination 7
REST Remote Explosive Scent Tracing 10
RTFK Revenue Tonne Freight-Kilometres 1
SOT Milestone: Shipments on Time 25
SPL Hub Schiphol Hub of Air France – KLM Cargo 1
SPT Shortest Processing Time 17
STD Scheduled Time of Departure 29
TAS Truck Appointment System 20
TNO Netherlands Organisation for applied scientific research 2
TPT Throughput Time 30
T-ULD Through Unit Loading Device 9
ULD Unit Loading Device 6
iii
Executive summary
KLM Cargo functions as the freight handling division of KLM, which handles more than 700,000
tonnes of freight every year. Cargo transported from other continents by airplane to Schiphol through the
SPL Hub belongs to the import cargo stream. Cargo arriving from Europe on its way to other continents is
considered to be export cargo. Two-thirds of the total export freight volume of KLM Cargo is gathered
from outstations all over Europe. Outstations are warehouses which are operated by third party handling
agents and are used to collect cargo from local customers. The hundreds of thousands of tonnes of cargo
handled every year, are transported by third party road feeder services (trucks) to Amsterdam Airport
Schiphol where it is collected and redistributed to other continents. The point of unloading, collection and
redistribution of all shipments of KLM Cargo at Schiphol is called the SPL Hub and operates as a cross-
dock.
The current truck arrival process at the SPL Hub can be divided into multiple sub-processes consisting
of the identification and authorisation of the truck driver, a screening procedure for unsecured cargo,
parking and driving on the hub’s terrain and unloading. Currently, all trucks are handled on a First Come
First Serve (FCFS) basis.
The performance at the SPL Hub is measured by a Key Performance Indicator (KPI) called Flown as
Planned. This KPI indicates the amount of shipments which were transported on the planned flights or
earlier than planned. This KPI is used to communicate the performance of KLM Cargo to customers and
other stakeholders. Customers of KLM Cargo are freight forwarders who collect cargo from shippers and
arrange all the transport legs of the supply chain until the final destination.
Currently, three problem areas can be recognized which cause an undesirable low value for the Flown
as Planned (FAP) KPI, which are: (1) ineffective truck schedules, violation of set delivery times by
customers and inefficient truck loading at outstations; (2) inefficient warehouse operations at the SPL hub;
and (3) a lack of control on truck streams from outstations to the SPL Hub. The latter problem area is
determined to be the research scope and is specifically defined as: ‘the transit flow from the outstations to
the SPL Hub and specifically from departure at the outstation until unloading at the SPL Hub’.
Data analysis performed during this study shows a clear positive relationship between (1) the time
period between unloading until flight departure time (“connection time”), and (2) the probability that a
shipment has Flown As Planned (positive FAP). To enhance the shipment connection time, within the scope
of this research, the processing time and waiting time of a truck containing this shipment should be
minimised. This should be done specifically for shipments that have a short connection time.
Truck scheduling has received quite some attention in container terminals industry and cross-docking
literature, however not specifically in the air cargo environment, where deadlines are tight. A more
iv
operational approach than refinement of a truck schedule, is the use of job scheduling. Literature on job
scheduling shows that four priority rules or sequencing strategies are commonly applied in order to
influence the throughput time of a job. This job is used as an analogy for the incoming shipments. The
priority rules are the following: FCFS (minimises variance in throughput time), Earliest Due Date (EDD -
minimises lateness of a job), Shortest Processing Time (SPT - minimises average throughput time by
minimizing average waiting time), and a variant on the commonly used Moore’s algorithm (minimises
number of tardy jobs).
As an addition to the current literature, a combination between job scheduling and future information
on job arrivals has been examined, in order to show the negative or positive effect of a higher level of
information that is available. These two aspects, especially within the air cargo industry, have not been
studied previously.
A combination of the stated priority rules and three levels of information that is available on the truck’s
location results in 9 future design scenarios.
To test these future designs, discrete event simulation modelling has been applied. 4 months of
historical data (Feb., Mar., Apr. and May 2017) have been used as an input to the model. The verified and
validated model on the current state handling process at KLM Cargo, has been used as a performance
baseline to the other scenarios.
The overall conclusion on this research is that EDD and Moore’s algorithm sequencing strategies are
beneficial for the FAP value, especially during the weekends. An increase of the level of information
amplifies this positive effect to a certain extent, due to the increasing waiting time involved with the level
of information. Although the amount of high potential shipments is limited, because the vast majority of
the cargo has a large connection time on arrival, sequencing strategies are able to save 94% of high potential
lost shipments.
The advice to KLM Cargo is, based on the model results from this research project, to perform a follow-
up study on the financial consequences involved with the implementation of a truck sequencing system.
With that, it is advised to start to investigate Scenario B and Scenario D which require the lowest investment
costs and are the least complex to implement. Furthermore, advised is to rerun the model in the future, when
process optimisations have been implemented at outstations, and new data is available.
Additionally, two recommendations can be made based on the insights gained by this research project:
(1) a review of the currently used transit times, which seem to be too short now, and finally (2) transferring
the process of securing shipments to outstations in order to reduce truck processing time.
Future research by KLM Cargo could be on: (1) the usage of a truck sequencing system for high value
shipments, (2) the implementation of a mobile application for truck drivers to enhance truck stream
visibility.
1
1. Introduction
Koninklijke Luchtvaart Maatschappij (KLM) has officially been founded as a Dutch airline in 1919,
with its first flight flown in 1920 from London to Schiphol (Air France - KLM Group, 2016b). The first
freight building of KLM is commenced in 1967 at Schiphol, whereafter the second and third building were
finished respectively in 1982 and 1991. After the merger in 2004 with the major French airline Air France,
KLM is part of the Air France – KLM Group.
Since 2008, the airline Martinair is fully owned by Air France – KLM, hence the present name of its
cargo division: Air France KLM Martinair Cargo (KLM Cargo). 457 destinations are accessible from the
two hubs in Paris Charles de Gaulle and Amsterdam Airport Schiphol by operating more than 170 aircraft
and an extensive European trucking network (Air France - KLM Group, 2017). In total Air France KLM
Martinair Cargo employs 4,600 people worldwide, transports around 1.2 million tonnes of cargo and has a
combined revenue of 2.5 billion euros (Air France - KLM Group, 2016a).
With a headcount of 1200 and a total operation area of 68,000 m2, the Schiphol Hub (SPL Hub) of Air
France – KLM is a major cargo handler at Schiphol (Air France - KLM Group, 2016). In 2015, more than
700,000 tonnes of cargo were carried with a revenue tonne freight-kilometres (RTFK) of 5,429 million.
RTFK is a unit of measure of freight transport.
In 2016, KLM Cargo rearranged their truck arrival area in front of the air cargo terminal (SPL Hub) to
improve the safety level. Cargo delivered from outstations is transported by third party trucking companies
contracted by KLM Cargo and is handled in Freight Building 3. This is one of the three buildings used by
KLM Cargo. A total of 17 different trucking companies and subcontractors are currently transporting goods
from between 80 and 100 outstations directly or indirectly to Schiphol. A large part of the trucks are claimed
to be too early (around 70%), 20% of the trucks arrive too late, meaning only 10% of the trucks arrive on
time. Close monitoring and control of the arriving trucks is currently lacking and trucks are handled with a
FCFS service policy. Deadlines are missed, according to several problem owners, due to the current service
policy at peak-hours. The KPI on the amount of ‘Flown as Planned shipments - FAP’ is currently around
80 percent at the SPL Hub. This KPI is used to communicate the air carrier’s performance to customers.
An increase by 1 percent, which will be shown later, will lead to a high increase in revenue. To enhance
this figure, more shipments need to reach the minimum shipment connection time upon unloading, which
should result in a higher output performance of the warehouse. The currently used minimum connection
time of 5 hours on arrival is based on the required process time between arrival of the truck and the departure
of the aircraft.
2
KLM Cargo provided the assignment to investigate the current state of the handling performance of
trucks arriving mainly from outstations in Europe at the SPL Hub, and to eventually design an optimized
arrival handling system to increase the FAP.
Earlier research done by the Netherlands Organisation for applied scientific research (TNO) in 2015
(Van Merrienboer, 2015) shows the possibilities and gains of a real-time smart data platform for logistics.
By combining data from several sources and analysing this data, smart applications could enhance
efficiency. Four information steps are necessary to enable smart data: real-time data availability, data
analysis & algorithms, information sharing and control on the supply chain. From May 2012 until April
2015 a European project called ARTEMIS used DEMANES technology the goal being to produce tools for
designing, monitoring and operating large embedded network systems (Artemis, 2015). A pilot was done
in the port of Rotterdam using this technology to reduce trucking queues, waiting times at the terminals and
truck turnaround time. By using two prediction models it was possible to predict peak-hours more
accurately. Trucking companies were able to adjust their planning upon this prediction to enhance
efficiency. This could also be an option for KLM Cargo for a few of their truck streams.
In the past, local initiatives at Amsterdam Airport Schiphol were already taken to reduce queues at the
different stages in the supply chain. Traditionally, every forwarder picks up their own freight at the shipper
resulting in a lot of truck movements, half-full trucks and queues at air carrier handling agents. Another
TNO initiative joined by Air Cargo Netherlands (ACN), resulted in the so called and award-winning
Milkrun project (ACN, 2016). Inspired by the old-fashioned milk trucks which collected milk from different
farmers, this project essentially arranged the same logistical setup only now with freight. By enhancing
cooperation between different logistical parties, less throughput time is created and a more efficient way of
working is realised. To monitor performances of all parties in the supply chain, a tool was built which is
able to show real-time data on throughput time, waiting times, truck utilisation, fuel consumption and CO2
reduction by making use of the Milkrun concept.
Practical and scientific value
The current performance of KLM Cargo is increasingly questioned by customers and needs to be
improved on short notice. Truck congestion and inefficient scheduling at the SPL Hub are presented by
KLM senior management as a great cause for concern which needs further investigation. This research
gives insights on possible benefits of using sequencing strategies to control truck movements on and around
the SPL Hub. Additionally, the report will fill the gap between the current process improvements at the
outstations and at the SPL Hub’s warehouse.
This research project has scientific value because it shows the effect of using priority rules on jobs with
multiple deadlines (truck contains multiple shipments with different due dates). This is a topic which has
3
little attention among studies on scheduling. Furthermore, this research project provides insights on the
impact of combining priority rules with different levels of information on future arriving jobs, being an
addition to the current literature. Strategic truck scheduling in crossdocking is no uncommon subject, but
operational truck scheduling, especially within the air cargo industry, had little attention yet.
Outline
The first part of the report will give an introduction on the research subject with additional background
information to understand the air cargo supply chain. This includes the research outline, research scope,
and relevant literatur on the subject of truck and job scheduling.
The second part contains a description of the current state of the truck arrival handling process, which
includes a process analysis and current performance analysis.
Part III contains the future state of the truck arrival process at the SPL Hub. Multiple scenarios are
created and modelled by using simulation techniques in order to assess the performances of the solution
designs.
The final report part, part IV, will provide the research conclusions, recommendations and is concluded
with a research discussion.
4
Part I
5
2. Introduction to air freight transportation
To be able to understand the solutions designed in this research project, it is necessary to grasp the full
concept of ‘air freight transportation’. This chapter provides a brief introduction on the air cargo supply
chain which gives insights on the involved parties and KPIs during a shipment from shipper to consignee.
Furthermore, a description is given on the different truck streams that operate at the SPL Hub of KLM.
2.1 The supply chain
Transporting cargo all over the world can be done in various ways. For example, by land transport (rail
or road), water transport (inland, short sea or ocean shipping) or by pipeline. Cargo that needs to be
transported over a long distance with a high time urgency, can also be transported by air. Since this transport
mode is relatively expensive, only cargo with a high value and a critical delivery time are transported by
air.
KLM Cargo transports a wide variety of goods, such as airmail, live animals, express parcels,
perishables, pharmaceuticals, valuables, technical supplies and luxury consumer goods. Customers of KLM
Cargo are postal companies, couriers, but primarily forwarders. Air cargo forwarders provide a door-to-
door supply chain solution for a shipper where an airline account for the airport-to-airport supply chain
part.
KLM Cargo is a member of the Cargo IQ group initiated by IATA, which enhances the visibility of the
air transportation supply chain by defining milestones during the shipment’s trip and performance
measurements for every subpart of the chain (IATA, 2017). The supply chain parties involved in air freight
transportation are, at a high level, described in Figure 1. A detailed description of the air cargo process is
given in Appendix A – Detailed air cargo process. The remainder of this chapter part contains the most
relevant subjects of this appendix.
Shipper’swarehouse
Forwarder’sbranch facility
Handling agent’s warehouse/hub
Airport BHandling agent’s warehouse/hub
Forwarder’shub
Airport A
Forwarder’shub
Forwarder’sbranch facility
Consignor
Figure 1. Air freight supply chain parties (high level)
6
Milestones
An Airport-to-Airport Route map is used between shipping parties, which is a tool for supply chain
parties to monitor and control the shipment process (e.g. in Figure 2). On this route map, different
milestones in the air carrier’s supply chain are shown and can contain three different colours. A green
milestone means that the shipment has arrived in time, or with some offset at that stage. Red indicates that
a milestone has failed i.e. shipment is delayed. Blue indicates a future milestone. For every milestone, the
planned time and the actual time is indicated. These milestones show what part of the shipping process had
setbacks. The most important milestones for this research project are described below, starting with the
process at the handling agent’s warehouse.
Figure 2. Cargo IQ route map for one shipment showing the successful and unsuccessful milestones
Once the cargo is unloaded at the handling agent, it is temporarily stored in the warehouse, brought to
the break-down area when palletised or brought to the build-up area. This warehouse process is coordinated
by earlier made allocations on shipments to flights. A Freight Booked List (FBL) determines which
shipments need to be picked and build upon Unit Loading Devices (ULDs) at the build-up area for a single
destination (IATA, 2016). ULDs are large metal plates where shipments can be stacked on and is used as a
standardised piece of equipment in the air cargo industry. As soon as all ULDs for a single flight are
finished, a flight manifest is created with information on the weight and size of the ULDs. The air carrier
uses, amongst other documents, this manifest to create the aircraft load plan.
The ULDs are transported to the hold area where they await ramp transportation to the aircraft. When
the ramp transportation deadline is reached, the ULDs are brought to the aircraft parking position. The
cargo is loaded onto the aircraft as described in the final load plan and any discrepancies between the actual
load plan and the transported cargo are listed. Passenger aircraft can take a relative small amount of cargo
underneath the passengers in the so-called aircraft belly. Another option is to transport cargo in a full freight
aircraft and in this case both the main deck and the belly are used to store cargo. Finally a hybrid transport
solution of these two options is air transportation of cargo by a combi aircraft where one half of the aircraft
is occupied by passengers and the other half is filled with cargo.
7
Once discrepancies are resolved, the flight will depart and another Cargo IQ milestone is reached: DEP.
This milestone indicates the departure of cargo at the airport of origin. An important KPI of the air carrier
is calculated by the achievement or failure of this milestone. This KPI, which will be explained at a later
stage, is used for communication purposes towards the customers on the air carrier’s performance.
After the flight has departed, all necessary information on the cargo is distributed to the down line
stations and authorities at the destination airport. One of these messages is the Freight Forwarding Message
(FFM) which contains exactly the content of the on board ULDs.
At arrival, an ‘arrival at the airport of destination’ milestone is created: ARR. At this time a mirrored
process will occur. The cargo is unloaded from the aircraft and is moved to the warehouse. After the
shipments are received at the warehouse, an RCF milestone is created. Transit cargo will be stored in the
warehouse and re-enter the process at the start of the airline supply chain.
The forwarder at the destination is being informed through a message that the cargo is ready to be
picked up at the air carrier’s warehouse. A Notified for Delivery (NFD) message is in this case created and
a certain time period later the cargo will be picked-up by the forwarder.
This subpart of the total supply chain is illustrated in Figure 3.
Shipments are accepted by the
air carrier s handling agent
Shipments are sorted, stored and build on
ULDs
ULDs are transported to
the aircraft
ULDs are loaded on the aircraft
Aircraft departs from airport A
Aircraft arrives at airport B
ULDs are transported
from the aircraft to air carrier s handling agent
DEP
ARR
Freight Forwarding Message (FFM) is
sent to the handling agent
FFM
Handling agent receives the
ULDs
RCF
Shipments are sorted and
stored
A message is sent to the
forwarder that shipments are ready for pick-
up
NFD
Forwarder arrives at the
handling agent
Figure 3. Shipping: handling agent and air carrier
2.2 Air cargo terminal truck streams
At KLM Cargo, shipments are delivered to the SPL Hub of KLM by truck or by aircraft. It is possible
that cargo is transferred from flight to flight, just like passengers, and in most cases this could be from truck
to aircraft. The truck has in that case also a flight number and obtains exactly the same milestones as shown
in the previous chapter. If cargo is transferred from plane to plane it is referred to as being transit cargo and
this also holds for cargo that is transported from plane to truck and truck to plane.
Before a truck arrives, the handling agent prepares & plans for handling and storage of the shipments
based on the confirmed bookings and handling instructions. Deliveries by truck can be made by local
8
customers from The Netherlands or by customers from other outstations. Outstations are warehouses
positioned in other European countries and a few places in The Netherlands, which are used as collection
points for local customers. A booked route consists, in the case of delivered cargo at the outstations, of
multiple flight legs from origin to destination (e.g. Madrid-Amsterdam-New York). In this example, the
first leg from Madrid to Amsterdam is transported by truck and the second leg from Amsterdam to New
York is transported by aircraft. This scenario is shown in Figure 4.
Figure 4. Route from Madrid to Amsterdam (trucking) and from Amsterdam to New York (Flown). Red markers indicate origin
and destination, orange markers indicate alternative stops and grey dots indicate outstations.
Transit and export deliveries are processed a little bit differently at the SPL Hub. For local customers
from The Netherlands, KLM Cargo needs to ‘accept’ the goods. This is done by evaluating, amongst others,
weight, size and necessary documents of the shipment upon arrival. In this case, KLM Cargo operates as
the handling agent. The cargo from outstations is accepted by local third party handling agents at the
outstations and is transported by truck to Schiphol. The cargo transported from these outstations is, as
mentioned earlier, transit cargo.
Once the truck arrives on the agreed time, the documents need to be checked and the driver awaits
approval for unloading. After a suitable dock door is available to unload a new delivery, the truck is
appointed to a door. The shipments from the truck are evaluated against the booking on number of pieces,
weight and volume. The shipment is registered as received if all checks are approved and a message is sent
to the customer. The unloaded cargo is stored in the warehouse, store shipment documents are created and
a flight manifest is made. The cargo pieces are, after storage, gathered for the flight, according to the
booklist. The gathered goods for one single destination are built the ULD. The cargo is eventually
transported to the aircraft’s ramp some time in advance to the departure time. At the ramp, the cargo is
loaded on board of the aircraft.
Four main truck streams can be distinguished: trucks from outstations (2.2.1), local deliveries by freight
forwarders (2.2.2), trucks to the outstations (2.2.3) and local pick-up by freight forwarders (2.2.4). A
9
detailed process description is given in the following sections. Appendix C – Truck streams at the SPL Hub
shows in a flow chart at which of the three stations of the SPL Hub a truck is handled.
2.2.1 Export cargo stream from outstations
Freight forwarders are the biggest clients for airlines in the air cargo industry. Forwarders collect their
freight from shippers and consolidate it at their warehouse. Typically, cargo volumes from shippers increase
until the end of the week due to production schedules of manufacturers and truck efficiency towards the
handling agent. Once a certain limit of collected cargo is reached at the forwarder’s warehouse, the cargo
will be transported to the handling agent of the airline.
KLM Cargo has third party handling agents at the outstations in Europe and The Netherlands that accept
the cargo. Throughout the week and mostly at the end of the week, cargo is transported by trucks booked
by KLM Cargo from the various outstations to the SPL Hub in Amsterdam. Trucks are scheduled on
outgoing flights with a scheduled connection time to the connecting flight of 5 hours i.e. truck should arrive
at least 5 hours in advance. The driving time is based on the distance between the outstation to the hub,
average driving speed and additional slack time. The load of the truck and the documentation, also known
as an Air Waybill (AWB), is checked on arrival whereupon the truck is sent to the docks to unload. The
AWB is a transport document which describes the details of the shipment. The truck load is sent either
directly to the break-up area or it is stored. This depends on the time left to departure and the type of ULD.
Some ULDs contain cargo for multiple destinations and are called mixed-pallets (M-ULD). Other ULDs
have cargo for a single destination and are called Through-pallets (T-ULD). M-ULDs need to be broken
down, sorted and build up for a single destination. T-ULDs are temporarily stored and are sent down from
the pallet system shortly before departure. These pallets arrive at Schiphol and contain only shipments for
a single destination. Finally the ULDs are transported by trolleys to the aircraft to be loaded. The process
is illustrated in Figure 5.
Client/Customer brings cargo to handling agent
Handling agent collects and accepts
cargo
3PL provider (RFS) transports the cargo
Cargo arrives at SPL hub
Cargo is unloaded
Cargo is directly transferred to
break-up area or stored
Cargo is broken down, sorted and build up on ULD or directly transferred
ULDs are transported to the
aircraft’s ramp
Figure 5. Flow chart transit cargo from outstations to SPL Hub
10
The darker coloured process steps depicted in Figure 5 are shown in more detail in Figure 6. The truck
driver parks at one of the 17 parking spots before entering the hub terrain. The transit cargo from the
outstations could have already a secured status. If this is not the case, the cargo has to be checked before
entering the hub. The procedure to do so is called a ‘Remote Explosive Scent Tracing’ (REST) check, in
order to make sure no explosives are on board of the truck. Once the cargo has a secure status, the driver
has to proceed to the documentation office for document processing. After the documents are checked, the
truck is allowed to park on the hub’s terrain in front of the unloading doors for palletised cargo, also known
as Moving Truck Docks (MTDs). When an applicable unloading door becomes available, the truck
continues to this unloading spot and starts unloading. After unloading, the truck leaves via an exit gate. The
flow on the hub area is one-way. Appendix B – Truck arrival process, shows the truck arrival process in a
3D visualisation of the hub area.
Truck arrives and parks
Driver checks if REST
procedure is necessary
Cargo secured?
REST procedure
NO
Driver checks in at the
documentation office
YES
Driver is helped by
documentation clerk
Truck is assigned to parking spot
near the docks
When a door is available, the truck parks at the dock door
Truck unloadsTruck leaves
the hub
Figure 6. Flow chart truck arrival at SPL Hub (transit cargo from outstations)
2.2.2 Export cargo stream from (Dutch) forwarders
The process steps of the cargo stream from Dutch forwarders do not differ much from the transit cargo
stream. The main difference is that the arriving cargo needs to be ‘accepted’ on the SPL Hub. KLM Cargo
is in this case the handling agent in the supply chain. For transit cargo, the acceptance process is done at
the outstations by third party handling agents. The acceptance process requires additional checks on freight
documentation and, during unloading, on the physical cargo.
The doors used for export cargo mostly differ from the doors used for transit cargo, because of the way
the cargo is delivered. Export cargo is mainly delivered as ‘loose’ cargo, instead of palletised cargo. This
difference results in different door usage of these two streams. Although export cargo is overwhelmingly
11
non-palletised, it might occur that clients do have palletised cargo. In this case, export trucks are unloaded
at the same docks as trucks with transit cargo.
2.2.3 Import cargo stream to outstations
The import stream to the outstations at the SPL Hub works in the opposite direction of the export
stream. ULDs are transported from the inbound aircraft to the hub. On arrival, the pallets are broken down
and sorted on a single destination. Around 50 percent of the arriving cargo has a final destination in The
Netherlands and the other 50 percent of the incoming cargo is transported via booked trucks to the
outstations in Europe.
Import trucks have to pass the documentation office at the hub to do a document check, whereupon the
truck is appointed to a parking spot in front of the import freight building doors. Once a dock is available,
the truck will be loaded on EHS (European Handling System) doors. EHS doors are just like the MTD
doors at the export freight building, just for palletised cargo. After the truck loading is finished, the truck
leaves the hub via an exit gate.
2.2.4 Import cargo stream to local freight forwarders
Another import stream can be identified. In this case cargo is picked up by local freight forwarders
around Amsterdam. This other 50 percent of all import cargo is handled through freight building 1 and is
very similar to 2.2.3, but now the cargo is mostly loose and not palletised.
2.2.5 Similarities and differences between cargo streams on the hub
To be able to decide on the scope of this research project, it is important to picture the similarities and
differences of the converging processes at the hub (Appendix C – Truck streams at the SPL Hub). All
arriving trucks have to park at the entrance gate. In some cases, export and transit trucks need an additional
REST check to secure the cargo.
All truck drivers have to perform a documentation check, but the duration and process steps at the
documentation office might differ. At the moment, there are no official dedicated desks for certain arrival
streams. Despite this, information from observations show that export documentation handling and transit
documentation handling are processed separately by different employees. Truck drivers are served on a
FCFS basis.
After the documents are checked and approved, the trucks are allowed to continue to the applicable
freight building. Usually, transit trucks are unloaded at freight building 3 on MTD docks, but results from
interviews with managers reveal that, as an exception, it might occur that transit trucks are unloaded on
EHS docks during peak-hours. However, preliminary data analysis showed that only a small number of
trucks are unloaded at the EHS docks. The local export truck stream is directed to freight building 3 where
12
the trucks are unloaded on dock doors for loose cargo. The majority of the export trucks only carry loose
goods. Some forwarders already palletise the freight, which should be unloaded on the MTD doors for
unloading palletised cargo.
13
3. Research outline
3.1 Problem definition
The core business of KLM Cargo is to transport shipments, which are accepted by third party handling
agents at outstations or locally by KLM Cargo, on time. The indicator of on time performance is the Flown
as Planned KPI. Interviews with directors of the sales department reveal that a one percent change in FAP
can result in a yearly increase/decrease of several millions1 in revenues. Therefore, improving this KPI has
a high priority for KLM Cargo. The main causes for a decrease in FAP are shown in Appendix D – Cause
and effect diagram and are elaborated upon the sections below.
Data is gathered on the reasons of rebooking cargo on a different flight than actually planned. These
causes for rebooking, obtained from a data analysis on more than 6600 rebooked shipments, originate from
different problem areas. The reasons given cannot always be interpreted as the root cause, so further analysis
is necessary. The main exception messages are shown in Figure 7. The percentages could be misleading
because of the possibility of one reason having multiple root causes. Nevertheless, these exception
messages are used to communicate discrepancies to other supply chain players. The reason for the
rebooking is noted by a manual entry by the employee who actually made the rebooking. This could be a
cargo planner or a person from revenue management. Interviews with project managers reveal that the noted
reason might deviate because of a conflict of interest.
Figure 7. Main exception messages on rebooked shipments (data Cargo IQ. 2016: Dec; 2017: Jan & Feb)
1 This number is based on a comparison of the FAP performance of Air France Cargo and the FAP performance of KLM Cargo,
and the differences in yield. In this calculation, 20 points of FAP increase results in 60 million euro more revenues.
8%1,04%1,07%1,19%1,43%1,47%1,79%1,86%
3,74%3,77%
5,82%6,69%6,95%
7,43%9,26%9,37%
10,72%18,16%
Other causes
Incorrect buildup
Volume/Weight/Dims deviation
Lack of ULD or build up material
Rebooked/Reassigned in previous station
Flight cancelledBooked connection too short
Delayed truck departure due to late truck arrival (roundtrip)
Delayed flight departure
Location error/cargo not found
Delayed truck arrival
Payload issue (wgt/balance/fuel/baggage/aircraft change)
Buildup planning discrepancy
Rebooked/Reassigned due flight capacity constraints
Rebooked due capacity constraints (priority setting)
Delayed warehouse handling
Unexpected backlog (repair)
Breakdown planning discrepancy
14
Three problem areas can be identified which lead to a decrease in FAP: the warehouse processes at the
SPL Hub (3.1.1), the outstation processes (3.1.2) and the processes between the outstations and truck
unloading at the SPL Hub (3.1.3).
3.1.1 Causes from the warehouse processes
A lower than 100 percent FAP may arise from various causes. The major rebooking causes shown in
Figure 7, originate from inadequate warehouse operations, including planning of pallet break-down and
build-up. Planning discrepancies can be a result of many other causes, more than shown in the full cause
and effect diagram, such as earlier delays in the process.
One of the causes could be that the breaking down process in the warehouse started too late, which
resulted in a delay at the build-up stage. As a result, the cargo was not completed in time, to be transported
to the airplane. According to KLM managers and warehouse personnel, one of the key issues is that
workforce is not tuned to the amount of work that has to be done at a certain moment (Figure 8). This has
been revealed from interviews with not only managers, but also personnel working inside of the warehouse.
Workforce is planned on historical data from the year before, but recent months are a lot busier than last
years around this time2. This way of planning results in understaffing during busy moments at break-down,
transport and build-up. One can imagine that a capacity issue on workforce will result in congestion during
flight departure peak-hours. This congestion at the break-down stage will limit the operations later on in
the process.
Understaffing is not only a result of inadequate planning. Multiple interviews showed that through
every division and department of KLM budget cuts were made. In practice, this resulted in the loss of a
large share of the workforce and reduced budgets for many departments. Permanent employees had to leave
and if replaced, they were replaced by cheaper temporary employees. Experienced warehouse personnel
claim that the problem is not just the lack of number of people, but also the lack of available knowledge of
flex workers.
Currently, several initiatives are running to cope with this problem area. In the past break-down and
build-up were different departments which did not match the amount of work. A few months of
reorganisation in the warehouse resulted in a merger of these two departments. Currently running projects
should improve the work performance of the warehouse processes.
2 Retrieved from http://www.luchtvaartnieuws.nl/nieuws/categorie/2/airlines/goed-begin-2017-voor-luchtvracht on 07-04-2017
15
Low FAP KPICargo did not fly on planned flight
Cargo was not available for platform transportation
Break-down started too late
Not enough workforce
Limited workforce
Inadequate workforce planning
Through pallet was not available on time
Limited budget
Pallet was not presented on time
Pallet break-down planning discrepancy
Unexpected work/repairs on pallets
Rescheduled due to priority setting of other cargo
Build-up started too late
Pallet build-up planning discrepancy
Pallet was (temporarily) lost in the system
Lack of available build-up material
Volume, weight or dimension deviated from planned
Figure 8. Cause and effect (warehouse operations)
Workforce and operational problems inside the warehouse belong to one cause area which could result
in a delayed break-down of cargo. Another cause for a late break-down, is the unavailability of cargo at the
break-down deadline. Interviews with warehouse planners, which are responsible for the ULD break-down
schedule, show that planned ULDs cannot always be broken down because they are simply not yet present
in the Pallet Container Handling System (PCHS). Cargo which is unloaded too late at the docks, generally
results in a late break-down or in the case of a T-pallet in a late availability for platform transport. Since
this problem area is not within the scope of this research, further research is required on this part.
3.1.2 Causes from outstation processes
On the other end of the air carrier supply chain for transit cargo, is the handling agent at the outstation.
Although many causes are linked with operational planning concerning warehouse processes, cargo and
truck planning at the outstations seem to belong to another problem area.
The cargo from outstations is prepared and build on ULDs, ready to be picked-up by scheduled trucks.
One of the causes of a late truck arrival at the docks is a late truck arrival at the SPL Hub, which eventually
lead to a lower FAP. Trucks are scheduled with some additional (slack) time, but can have major delays
due to external factors such as weather, traffic jams or truck breakdowns. Another reason is that some of
the cargo that is loaded on the trucks at the outstations already has a planned connection time which is too
short whether the truck is on time or not. Finally, a reason for late truck arrivals at the hub is the late
departure at the outstations. These three causes for late truck arrivals at the hub are depicted in Figure 9.
Currently, a project is running to solve the issues at the outstations. This project should result in a better
planning of cargo on trucks (e.g. more T-pallets), enhanced truck schedules and shorter shipment
connection times.
16
Earlier data analysis shows that 6 percent of all rebooked shipments is because of delayed trucks which
could potentially be more if the rebooking cause is noted as a planning discrepancy.
Low FAP KPICargo did not fly on planned flight
Cargo was not available for platform transportation
Break-down started too late
Through pallet was not available on time
Cargo was unloaded too lateTruck arrived too lateat the unloading dock
Truck arrived too lateat the hub
External factorsduring transport
Truck left outstationtoo late
Cargo loadedwhich has too short
connection time
Build-up started too late
Figure 9. Cause and effect (late truck arrivals)
3.1.3 Causes from terminal yard processes
Another case is that trucks arrive on time at the hub, but still unload their cargo too late at the docks
due to congestion during peak-hours (Figure 10). Interviewees claim that different truck flows arrive at the
same time during the week, which results in peaks (which will be verified in Chapter 5). Given the current
limited unload capacity at the SPL Hub and the arrival of trucks in peaks, congestion at unloading is
unavoidable. At Amsterdam Airport Schiphol, flights are scheduled to optimise the hub and spoke network.
This type of network optimisation results in closely arriving and departing airplanes. Truck schedules are
partly based on flight schedules to obtain the most connecting flights. Besides, the force of the market
causes a problem that is unavoidable. Market force is another reason why shipments arrive in peaks. Many
shippers want to clear out the warehouse just before the weekend, so the larger part of the weekly shipments
are delivered at handling companies on Fridays.
Low FAP KPICargo did not fly on planned flight
Cargo was not available for platform transportation
Break-down started too late
Through pallet was not available on time
Cargo was unloaded too lateTruck arrived too lateat the unloading dock
Congestion at the hub
Trucks arrive in peaks
Limited capacity at the hub
Transit trucks are scheduled in peaks
Multiple streams converge at process steps
Flights leave in peaks due to hub and spoke
Build-up started too late
Figure 10. Cause and effect (congestion at the hub)
The way how to deal with congestion at the hub during peak-hours is until now rarely investigated by
KLM Cargo. For this reason, this will be the scope of this research project.
3.2 Research scope
Interviews with department directors, department managers, project managers, project members, data
analysists, business analysists and cargo employees gave the first angle for this investigation. Completed
by own observations and data analysis, a research question and scope can be formulated.
The scope of the research project is determined to be the transit flow from the outstations to the SPL
Hub and specifically from departure at the outstation until unloading at the SPL Hub. This because of the
following reasons:
17
The transit flow from European outstations contains around two-thirds of the total export flow at
the SPL Hub;
Currently, the Flown as Planned KPI at the SPL Hub is too low which is caused for a large part by
transit cargo;
Several projects along the supply chain try to enhance the transit process, but none focus on the
truck process at the Hub’s yard;
Optimised truck schedules and arrivals from outstations will always contain an amount of
uncertainty due to external factors. It is therefore important to be able to act on this;
Currently, trucks containing cargo that have a small connection time could possibly miss their
connection due to the current service policy during peak-hours;
The possible gain in the FAP by increasing the connection time one to two hours is approximately 1.4
percent. (Appendix E – Calculation increase in FAP)
From the preliminary data-analysis and interviews, it is revealed that increasing the connection time
will result in a higher FAP for shipments with a small connection time. In order to increase this connection
time within the scope of this research, it is necessary to decrease the throughput time for these shipments.
This research project give insights on the possible design options that KLM Cargo has, to decrease the
throughput time for shipments with short connection times.
3.3 Research questions
To get the scope clear a research question is defined. The main research question for this thesis project
is as follows:
Main research question
“What design options does KLM Cargo have to increase the FAP, by using truck sequencing
strategies with different levels of location information availability?”
Sub research questions
1. How is the current arrival process at the SPL Hub designed, which truck streams can be identified
and which internal and external stakeholders are involved?
The answer to this question will provide a clear view on the shipment processes between the outstations
of KLM Cargo and the SPL Hub. An analysis of all truck streams of the hub will show possible bottlenecks
of the process where multiple streams converge. All internal and external stakeholders are taken into
account and described to be able to change processes at a later stage.
18
2. What is the performance of the current truck arrival process at the SPL Hub for transit cargo and
how can it be influenced?
To get a baseline of the process, the current performance is measured. This part will describe the current
SPL Hub KPIs, the current hub throughput time and truck arrival performances of the different truck
streams.
3. What sequencing strategies could be applied in order to increase the connection time of transit
cargo?
The current sequencing rule at the SPL Hub used to process arriving trucks is on a First Come First
Served (FCFS) basis. This strategy will be used as a baseline. Literature on operational scheduling for single
machine process shows that, depending on the management goal, different sequencing strategies could be
more beneficial than sequencing on a FCFS basis. The strategies often differ on flow time, earliness,
tardiness and number of tardy jobs. The following strategies are tested and used:
Earliest Due Date (EDD) – Trucks with the shortest connection time will be processed first.
Depending on the load of the truck, the connection time will be determined. This strategy can be
used if the goal is to minimise the maximum lateness of the shipments.
Shortest Processing Time (SPT) – Trucks with the shortest processing time will be scheduled
first. This strategy will only be useful if the processing times between trucks differ. This strategy
will result in the minimal mean throughput time.
Moore’s algorithm – Trucks are scheduled on an EDD basis, only now with a limitation on the
minimum due date. If the planned completion time of a truck is later than the due date, the shipment
with the largest processing time will be removed. This process will be iterative until the first batch
of trucks will have a planned completion time without tardiness. Moore’s algorithm will minimise
the total number of tardy jobs. A variation on this algorithm is used in this research.
All these strategies are limited with respect to the amount of constraints that are taken into account. In
the case of real life truck scheduling, additional constraints need to be added. This could for example be on
the type of cargo or on customer segment.
4. What is the effect size of the level of available information on truck locations?
Currently the exact location of a truck during the ride from an outstation to the SPL Hub is practically
unknown. In this research, three levels of information availability will be considered:
19
Level 1 - Current: In the current situation at the SPL Hub, truck locations are only available once
the truck driver reports at the documentation desk. This level of information availability will serve
as a baseline. Only small system adjustments will be necessary in order to link this data to other
systems.
Level 2 - Gate: The next level, level 2, assumes that the location of the truck becomes available as
soon as the truck enters the pre-hub parking area. This information could be obtained by taking the
information from license plate scanners which are already installed and the information sent by the
outstations which indicate the truck its license plate.
Level 3 - GPS: The highest level of available information assumes that real time data on truck
locations is available. In this case, sequencing can take place in advance of the actual arrival of the
truck. This assumption is not unfeasible within a reasonable amount of time. Multiple showcases
in other industries are available, to show the feasibility of such solutions.
In total, ten scenarios are modelled each with a specific sequencing strategy and a level of available
information on the trucks location and carrying shipments (Table 1).
Table 1. Research scenarios varying on sequencing strategy and level of available information
Level of available information
Sequencing Strategy No information Gate entrance Truck GPS
First Come First Served Scenario A X X
Earliest Due Date Scenario B Scenario E Scenario H
Shortest Processing Time Scenario C Scenario F Scenario I
Moore’s Algorithm Scenario D Scenario G Scenario J
Two possible scenarios using FCFS, indicated by an ‘X’, are not taken into account in this research
project. The outcomes on the FAP will be the same no matter the level of information that is available. This
is because the sequence of trucks will remain equal in any FCFS scenario, so no improvement will be
obtained there.
The last part of the research contains a conclusion and answer on the main research question. The final
results will be used as input for the implementation solution.
20
4. Literature review
The problem is believed to belong to two research areas: truck scheduling (4.1) and job scheduling
(4.2). An extensive amount of work has been done in both research areas, but only limited specifically for
air cargo. Because of that, other comparable industries are used to describe potential solution strategies.
4.1 Truck scheduling
The goal of truck scheduling is to obtain a feasible schedule for inbound and/or outbound trucks, with
an objective depending on managerial input (e.g. minimise total operation costs (Ou, Hsu, & Li, 2010)).
Feasibility is reached when all trucks are handled in time, in such a way that the scheduled cargo is loaded
on the appointed flight. Truck scheduling can be used to control the amount of arriving trucks at the terminal
and limit uncertainty, as to eventually optimise the assignment of trucks to the capacity constrained dock
doors.
4.1.1 Truck scheduling in an air cargo environment
The processes at an air cargo terminal are quite similar compared to an ordinary cross-docking facility,
but differ largely in the consequences for not meeting a deadline. Speeding up processes to get extra slack
time or more throughput is very desirable to the operational management. One way of gaining insights in
the processes is by doing simulation. This also holds for air cargo terminal operations.
Lee, Huang, Liu & Xu (2006) did a simulation study on the import flow of air cargo terminals. In the
model, different types of cargo are handled differently depending on the type of cargo.
Ou, Zhou & Li (2007) models, contrary to Lee et al. (2006), not only the import process at an air cargo
terminal, but also the export process.
An important way of reducing congestion at the hub is by lowering truck arrivals at peak-hours. Truck
scheduling seems to be a subject within the air cargo operations research area with little attention. Feng, Li
and Shen (2015) did a literature study on air cargo operations from the perspective of the airlines, the freight
forwarders and the service supply chain. The review shows that only two papers are written on truck
scheduling at air cargo terminals. The reason for this low number is not completely clear, but it could be
because of the similarities between air cargo terminals and cross-docking facilities.
Hall (2001) conducted the first research on truck scheduling at an air cargo terminal based on a project
with the objective being to create a real-time tool for scheduling trucks and the sorting process of the
packages. The designed tool can be used to see what the effects are of altering departure times to possible
starvation (low utilisation) or overloading the terminal system.
21
A more recent study on truck scheduling performed by Ou et al. (2010), shows the potential benefits of
alternative approaches compared to the frequently used FCFS approach. The scheduling of the trucks will
only be based on the flight schedule of outbound (export) flights
4.1.2 Truck scheduling in crossdocking
The process at an air cargo hub facility is comparable to cross-docking in warehousing. On the one
hand inbound trucks from multiple origins enter the system with cargo for multiple destinations, which is
unloaded at inbound dock doors, whereafter the packages are sorted per destination, or temporarily stored,
and finally being loaded onto trucks via outbound dock doors (Konur & Golias, 2013).
To structure complex research fields like scheduling, Boysen et al. (2010) show classification schemes
of research papers based on a tuple notation, which can be found in machine scheduling and queuing
systems, project scheduling and assembly line balancing and sequencing. With additional attributes from
cross-docking, the conventional machine scheduling can be used for truck scheduling classification. Three
basic elements describe the truck scheduling problem noted as tuple [α|β|γ]. α represents door environment,
β stands for operational specifications, and γ represents the objectives.
4.1.3 Truck scheduling in container terminals
Container terminals are, just like air cargo terminals, struggling with the high amount of trucks that
arrive simultaneously at the terminal (Ambrosino & Caballini, 2014). Truck congestion issues at a container
terminal gate can be solved from two sides: improvement of supply and/or control on demand (Guan, 2009).
By physical expanding the terminal’s gate and the improvement in productivity of the gate, the supply of
the terminal is optimised. Managing and optimising the inflow of the system i.e. truck arrivals, is getting
control on the demand.
One of the major solutions for managing incoming trucks at container terminals is the application of a
Truck Appointment System (TAS) (Chen et al., 2016). In a TAS, the terminal operator gives trucking
companies the opportunity to choose arrival hours as they prefer within time windows. The total number of
bookings within a time window is based on historic data of preferences of companies, but is limited. This
should eventually result in a more spread out demand, as certain time windows will be blocked after a
number of reservations. The terminal operator has the goal to spread the truck arrivals, but wants to
minimise the deviation between a preferred time window and the assigned time window (Zehendner &
Feillet, 2014). Although the application of a TAS is not uncommon, the effects seem to be mixed (Chen et
al., 2016). Applying a TAS is easier said than done and involves a lot a factors which need to be taken into
account to make it a successful solution to terminal congestion. Morais & Lord (2006) did a study on the
impact of multiple container terminal solutions in order to reduce congestion, delays and emissions. In their
study they came to the conclusion that in order to have a successful TAS, it needs to be flexible. The most
22
important factor of a successful TAS is the cooperation of many, or even better, all trucking companies.
The trucking companies have to make reservations in the available time slots and should respect these times.
One way of forcing trucking companies to make use of the TAS is to make it mandatory (Zehendner et al.,
2014), but the challenge then is how to act if a truck does not come on time i.e. handle disruptions in the
system.
Phan & Kim (2015) discuss a way of negotiating between trucking companies and terminal operators
to level truck arrivals during peak-hours. Another way of making it attractive to use certain time windows
is money i.e. time-varying pricing. Chen, Zhou and List (2011) extend on the research of road pricing and
use time-varying toll prices as an incentive for truckers to choose time windows that are cheaper and related
to less busy periods.
As stated earlier, spreading out the demand at terminals can seriously reduce congestion. Moving a part
of the demand during the truck arrival peak-hours has not been discussed before. Dekker, Van der Heide,
Van Asperen and Ypsilantis (2013) presented a Chassis Exchange Terminal (CET) concept that works
similar to an extended gate concept for container terminals. The extended concept relocates several
processes of a sea terminal to an inland terminal with excellent access for multiple transport means such as
trucks, trains and/or barges (Veenstra, Zuidwijk & Van Asperen, 2012).
4.2 Job scheduling
Incoming trucks with shipments can be seen as arriving jobs at a manufacturing process. Job scheduling
is a common way of optimising the process and is defined as:
“[…] is a decision-making process that is used on a regular basis in many manufacturing and services
industries. It deals with the allocation of resources to tasks over given time periods and its goal is to
optimise one or more objectives.” (Pinedo, 2008).
Three common sequencing strategies used are (Rajendran, 1999):
First in First Out (FIFO) / Arrival Time (AT): A job that entered the queue first, is chosen to be
processed. The FIFO rule is effective when the desire is to minimise the maximum throughput time
and variance of the throughput time.
Shortest Processing Time (SPT): Jobs with the shortest processing time are sequenced first. This
rule is commonly used and effective in order to minimise the mean throughput time and minimise
the average tardiness of jobs.
Earliest Due Date (EDD): This rule is applied often, because of its simplicity to implement and
understand. The EDD rule schedules jobs with the earliest due dates first. This rule is often applied
to minimise the maximum lateness of jobs.
23
Ramasesh (1990) did a literature study on job scheduling and combined outcomes and conclusions on
the performance of different priority rules. One of the outcomes was that the SPT sequencing rule,
performed best for minimising mean flow time. With a high processor utilisation rate (90%), SPT is able
to minimise the number of tardy jobs compared to other strategies. Furthermore, different studies show that
SPT should minimise the number of tardy jobs better than a due date based priority rule such as EDD.
Rajendran (1999) showed that FIFO minimised the maximum flowtime, EDD minimises the maximum
tardiness and SPT minimised the average throughput time.
An algorithm designed to minimise the number of tardy jobs is Moore’s Algorithm designed by Moore
(1968). This priority rule orders a queue with an EDD strategy, but if a job is added to the queue and will
not be completed in time, the job with the highest processing time is removed and added to another queue.
This queue is ordered in an arbitrary way.
4.3 Truck scheduling and Job scheduling
Boysen et al. (2013) describe the truck scheduling problem with fixed outbound departures in cross-
docking terminals. A set of outbound trucks, including their departure time and dock position, is assumed
to be known in advance. The departure time of these outbound trucks is fixed. These outbound trucks can
be compared to scheduled flights which also have a fixed departure time that is known in advance. So the
problem is reduced to the set of inbound trucks that could be unloaded at a set of doors. Unloading a truck
can be compared to a job, and a dock door can be seen as a processor. By doing this, the problem can be
interpreted as a traditional scheduling problem with parallel processors, with the objective to minimise the
weighted number of late jobs. Each inbound truck could contain multiple shipments with different
destinations and due dates. So each job could have multiple due dates. A situation is considered in which
quite a few trucks are queuing up, since they often arrive overnight. The objective is to minimise the total
value of delayed shipments.
Boysen et al. (2017) study the truck scheduling problem at a postal distribution centre (DC). The authors
aim to increase the sorting performance at the DC by assigning feasible dock doors and processing interval
to inbound trucks. In this situation, trucks cannot be scheduled prior to their arrival time at the DC. These
arrival times are continuously updated based on GPS navigation system information of trucks which are
connected to the scheduling system of the DC. The usage of GPS-based information is in line with multiple
research scenarios in this thesis project.
24
Part II
25
5. Current process performance
As this is a data driven research project, it is necessary to perform a thorough data analysis on the
current state. Figures such as the current KPI performances, process throughput times and truck arrival rates
are depicted to gain sound insights on the current process performance and challenges for KLM Cargo.
These insights can be used later on to design, model and verify new solutions to increase KPIs.
As described above, three main truck streams can be identified each which their own performance
indicators: transit cargo from outstations to the SPL Hub (5.1), local deliveries from Dutch customers (5.2),
and finally the import streams towards the outstations (5.3) and local freight forwarders (5.4).
The data covers February, March, April, and May 2017. A greater period is covered, subject to
availability of the relevant data. The data is described in Appendix F – Data description and availability.
If not stated otherwise, statistical tests performed on the data are done by using a z-test for proportions,
as described in Appendix G – Statistical testing.
5.1 Current performance from outstations
Cargo originated from the European outstations covers a large amount of the total export cargo
transported through the SPL Hub. Although the local deliveries are growing yearly, approximately two-
thirds of all shipments still originate from outstations (Figure 11).
Figure 11. Total yearly percentages of export freight (measured by weight) transported from outstations or by local customers
Relevant performances on this large truck stream, are described in the following sections.
5.1.1 Flown as Planned (FAP)
The main concern at the SPL Hub, according to the interviewees, is that the Flown as Planned (FAP)
KPI is not necessarily low because of the late arrivals of trucks, but because of the inefficient processing of
arriving trucks. The FAP is an overall used milestone of the Cargo 2000 (relaunched as Cargo IQ)
programme initiated by the International Air Transport Association (IATA). Cargo IQ is a self-funded
standard by major airlines and freight forwarders with the objective to “implement processes, backed by
31% 35%
69% 65%
0%
20%
40%
60%
80%
100%
2015 2016
Local Outstations
26
quality standards, which are measureable to improve the efficiency of air cargo”. Three main processes are
divided in multiple steps with milestones to inform subsequent parties in the supply chain. Export
forwarding is divided in 7 steps. Air transport and handling are also divided in 7 steps and import forwarding
in 5 steps.
2 milestones (ARR & DEP) are used on the truck routes to the SPL Hub to determine the KPIs:
Shipments on Time (SOT) and FAP. SOT indicates whether a shipment, transported by a truck, arrived on
time at the SPL Hub. The FAP indicates, as mentioned before, if a shipment left the SPL Hub on time. The
results of the two KPIs are depicted in Figure 12. The FAP value for on time shipments is significantly
higher than trucks that are not on time (p<0.01; n>10,000).
Figure 12. Shipments on Time (SOT) and shipments Flown as Planned (FAP)
One could assume that more connection time will eventually lead to a higher FAP value. To verify this
assumption, data is gathered on the rebooked shipments (not-FAP shipments) and the amount of connection
time that was left upon unloading (Figure 13). The percentage of FAP shipments that have only 0-2.5 hours
left until departure are removed due to the fact that the build-up team has to ‘close down’ the pallet building
stage at least 2.5 hours before departure. As of then, all the weights and sizes of the pallets have to be
briefed to the aircraft’s personnel in order to finish their aircraft load plan. The results show that the
maximum FAP value is at 82 percent, regardless of the connection time upon unloading. The overall FAP,
according to the earlier performed calculation (Appendix E – Calculation increase in FAP), is about 79.2%.
This figure deviates from the implicitly stated FAP in Figure 12, because the actual FAP value decreases
with high connection times as shown in Figure 13. However, the performed calculation assumes a non-
decreasing FAP probability for high connection times.
77% 69%
23% 31%
0%
20%
40%
60%
80%
100%
Shipment on Time Shipment not on Time
not-FAP
FAP
27
Figure 13. Amount of rebooked shipments against the connection time in minutes of a shipment upon unloading
An explanation for the 20 percent FAP score for shipments with approximately 3 hours connection time
could be that these shipments are mainly on T-pallets, which require no extra work at the hub. The ratio T-
pallets and M-pallets is about 54% (T) against 46% (M). From interviews it is revealed that T-pallets require
less processing time than M-pallets which require at arrival around 5 hours of connection time.
Data fitting is applied to the data used on the current FAP performance with shipment connection time
x, by using a non-linear least squares method between the lowest (150) and highest actual data point (630).
The least squares method minimises the distance between the fitted line and the actual data points, by
minimising the sum of the squares of these distances. The reason for using the squared distances is because
of the possible negative distance between the actual and the fitted data. Minimisation results in a polynomial
function presented in Equation 1.
The final equation used to calculate the FAP, has a lower limit of 150 minutes, which is the final cut-
off time at the end of the warehouse process. Values between 150 and 600 minutes of connection time at
unloading are calculated by a second degree polynomial function. Assumed is that more connection time
will not result in a decrease of the FAP. The polynomial function is for that reason cut off at 600 minutes
and increases by 0.1% per minute until 630 minutes of connection time. An upper limit is reached at a FAP
probability of 83%.
Equation 1. Shipment FAP - current state
𝑆ℎ𝑖𝑝𝑚𝑒𝑛𝑡 𝐹𝐴𝑃(𝑥) =
{
0 𝒊𝒇 𝒙 < 𝟏𝟓𝟎,−1
249,345𝑥2 +
1
233𝑥 − 0.3254 𝒊𝒇 𝟏𝟓𝟎 < 𝒙 < 𝟔𝟎𝟎,
0.80 +(𝑥 − 600)
1000𝒊𝒇 𝟔𝟎𝟎 < 𝒙 < 𝟔𝟑𝟎,
0.83 𝒊𝒇 𝒙 > 𝟔𝟑𝟎
0,00%
20,00%
40,00%
60,00%
80,00%
100,00%
O.K
Rebooked
28
Equation 1 is presented by the orange coloured graph in Figure 14. The ‘fitted line’ represents the fitted
polynomial function, used only for connection time values between 150 and 600. The actual data points,
obtained from historical data, are depicted by the blue coloured graph.
Figure 14. Fitted FAP equation of the current state
5.1.2 Shipment connection times
It is important to have insights on the amount of cargo that is arriving with short connection times,
because this will influence the average FAP value. Operational documents show that the connection times
differ per product. Three main products can be distinguished and can be delivered as loose cargo or on
ULDs. Table 2 shows the products regarding minimal connection times upon arrival. Around 89 percent of
the cargo products that are sold have an agreed transit connection time of five hours before departure. These
products differ on size and weight limits and may have different priority statuses. For this research, a
connection time of 5 hours will be used.
Table 2. Minimal shipment connection times for cargo products, shown as latest acceptance time before departure (Dep)
Product A Product B Other products
Loose Cargo Dep -1,5 hours Dep -3 hours Dep -5 hours
ULDs Dep -1,5 hours Dep -3 hours Dep -5 hours
% transit 2% 9% 89%
Figure 15 shows the daily distribution of all shipments on a certain day. The majority of the shipments
have connection times larger than 12 hours on arrival (± 68 percent). These shipments are called ‘cold
shipments’ and have a low priority for processing, due to their large connection times. Currently running
0%
20%
40%
60%
80%
100%
0 100 200 300 400 500 600 700
FAP
Connection time at unloading
Historical data Equation 1 Fitted line
29
projects on outstations show a significant decrease in cargo with more than 12 hours of connection time,
during pilot periods (<40%).
Figure 15. Distribution of shipments' connection time on arrival
One should be careful with drawing conclusions like: “63 percent of the shipments are cold, so 63
percent of the trucks are cold”. One truck contains multiple shipments divided over multiple ULDs. The so
called temperature of the truck is determined by the shipment with the least connection time. For example,
when a truck transports 100 shipments equally divided over 4 pallets: 3 pallets contain solely shipments
with >12 hours connection time and 1 pallet containing 20 of the 25 shipments with a connection time of
>12 hours. The remaining 5 shipments determine the actual ‘temperature’ of the truck and the possibility
could occur that these shipments have only 3-5 hours connection time.
5.1.3 Throughput time
The remaining connection time inside the warehouse is a result of the remaining connection time at
arrival minus the throughput time at the hub’s truck area. Shipments arrived at the hub will have a higher
remaining connection time by minimising the throughput time. The truck throughput time in this data is
defined as the time between the first card scan made by the truck driver until gate exit.
From the described process steps for transit cargo, it can be derived that a REST check is optional.
Figure 16 shows the average truck throughput time over the week for a secure and a non-secure truck. A
non-secure truck has to do a REST check at arrival and spends on average 20 to 30 minutes longer on the
hub. The throughput times on Wednesdays, Thursdays and Fridays show minor deviation between the two.
However, during the weekends, trucks spend on average 20 to 25 minutes longer on the same route.
Mondays should be a favourable day for transit truckers, since Monday has the lowest throughput time.
0%
20%
40%
60%
80%
After STD 0-4 hrs beforeSTD
4-5 hrs beforeSTD
5-7 hrs beforeSTD
7-10 hrs beforeSTD
10-12 hrs beforeSTD
>12 hrs beforeSTD
30
Figure 16. Truck throughput time at the hub (REST and NO REST)
Interviews with project managers at outstations claim that only 20 percent of the trucks are unsecure.
However, analysis on truck data shows other figures on the percentage of trucks that need to be checked.
Figure 17 shows that more than 50 percent of all incoming trucks need to be checked upon arrival. It also
shows that the relative and absolute amount of trucks that need additional security checks differs per market
region.
Figure 17. Percentage of trucks that need a REST check upon arrival. Divided per market region.
Hourly basis differences appear when zooming in on the truck throughput times for secured trucks
(Figure 18). A preliminary conclusion can be made that the throughput time increases during the peak-
hours at the hub, due to congestion and longer waiting times.
Figure 18. Minimum, average and maximum truck throughput time at the hub (NO REST - hourly)
0
20
40
60
80
100
120
Sun Mon Tue Wed Thu Fri Sat
Ave
rage
TP
T (m
inu
tes)
NO REST
REST
0,00%
10,00%
20,00%
30,00%
40,00%
50,00%
60,00%
70,00%
Secure Unsecure
UK & IRELAND
NORDIC
ITALY & Switzerland
IBERIA
GERMANY & AUSTRIA
FRANCE
CENTRAL EUROPE
BENELUX
0100200300400500
0004081216200004081221010509131721010509131721010509131721010509131721010509131721
Sun Mon Tue Wed Thu Fri Sat
Min
ute
s
Min of TPT Average of TPT Max of TPT
31
The possible gain in throughput time by prioritising trucks with a high throughput time would
approximately be one hour based on the difference between the minimum throughput time and the average
throughput time.
5.1.4 Truck arrival patterns
On average, each week 500 trucks arrive at the SPL hub from outstations in Europe (Figure 19).
Seasonality exists in the air cargo industry. Christmas, Easter, New Year, China’s New Year and summer
are all examples of seasonal influence on the amount of cargo transported throughout the world.
Figure 19. Actual truck arrivals at KLM Cargo SPL Hub per week (transit cargo)
The schedules of the transit trucks coming from the outstations are solely based on the flight
connections instead of based on the available capacity of the hub. Outgoing flights from Schiphol depart in
peaks because of the hub and spoke system that is mainly focussed on connection efficiency. The data on
scheduled truck arrivals confirms the expected peak patterns (Figure 20). On the night from Friday on
Saturday, many trucks are scheduled to arrive at the SPL Hub with the highest peak between 03:00-04:00
(24h). Saturday night on Sunday is the busiest night according to the schedule, with the highest peak of
1.70% of the total arriving trucks between 02:00-03:00 (24h). Mondays and Tuesdays contain the least
scheduled truck arrivals.
Figure 20. Scheduled truck arrivals at KLM Cargo SPL Hub (transit cargo)
0
100
200
300
400
500
600
201
6 -
w2
201
6 -
w4
201
6 -
w6
201
6 -
w8
201
6 -
w1
0
201
6 -
w1
2
201
6 -
w1
4
201
6 -
w1
6
201
6 -
w1
8
201
6 -
w2
0
201
6 -
w2
2
201
6 -
w2
4
201
6 -
w2
6
201
6 -
w2
8
201
6 -
w3
0
201
6 -
w3
2
201
6 -
w3
4
201
6 -
w3
6
201
6 -
w3
8
201
6 -
w4
0
201
6 -
w4
2
201
6 -
w4
4
201
6 -
w4
6
201
6 -
w4
8
201
6 -
w5
0
201
6 -
w5
2
201
7 -
w1
201
7 -
w3
201
7 -
w5
201
7 -
w7
201
7 -
w9
201
7 -
w1
1
201
7 -
w1
3
201
7 -
w1
5
201
7 -
w1
7
201
7 -
w1
9
201
7 -
w2
1
Arr
ived
tru
cks
0,00%
0,50%
1,00%
1,50%
2,00%
000408121620000408121721010509131721010509131721010509131822020610141822020610141822
Sun Mon Tue Wed Thu Fri Sat
32
The pattern of the scheduled trucks is expected to be similar to the actual truck arrivals. Figure 21
shows that the actual truck arrivals are more levelled and smoothened compared to the scheduled trucks.
Mondays and Tuesdays are the quietest days of the week, so this is equivalent to the scheduled arrivals.
Wednesdays, Thursdays and Fridays are equally busy. Friday on Saturday nights are still the busiest
periods, but the difference between Friday night and Saturday night is a lot bigger with the actual arrivals
compared to the scheduled arrivals. This is a consequence of positive and negative truck arrival lateness.
Figure 21. Actual truck arrivals at KLM Cargo SPL Hub (transit cargo)
Although the number of scheduled trucks are every week more or less the same, the number of actual
arriving trucks per hour on a day vary a lot when analysing the data (Figure 22). This could mean that the
arrivals contain a lot of uncertainty which has a strong effect on the complexity of workforce planning at
the SPL Hub.
Figure 22. Minimum, average and maximum of actual truck arrivals per hour at KLM Cargo SPL Hub (transit cargo)
The difference between scheduled arrival times and actual arrival times should result in lateness. This
fact is supported by the observed data shown in Figure 23. Around 70 percent of the trucks arrive too early,
with 50 percent arriving more than 1 hour too early. 17 percent of the trucks arrive more than 1 hour too
late compared to the scheduled arrival time. 34 percent of the trucks arrive within a 1 hour window of the
scheduled arrival time.
0,00%
0,50%
1,00%
1,50%
2,00%
000408121620000408121623030711151923030711151923030711151923030711151923030711151923
Sun Mon Tue Wed Thu Fri Sat
0
5
10
15
00 04 08 12 16 20 00 04 08 12 19 23 03 07 11 15 19 23 03 07 11 15 19 23 03 07 11 15 19 23 03 07 11 15 19 23 03 07 11 15 19 23
Sunday Monday Tuesday Wednesday Thursday Friday Saturday
Minimum Average Maximum
33
Figure 23. Lateness of trucks arriving at KLM Cargo SPL Hub
5.2 Truck arrivals from local customers (export)
Earlier research on export acceptance for cargo delivered by Dutch customers provides data on the
arrival of trucks of the export stream to the SPL Hub (Figure 24). During the weekend, truck arrival peaks
occur, with the highest peak occurring on Friday on Saturday night. This could be problematic as this is
also a busy period for the transit cargo export stream.
Waiting times for truck drivers at the documentation office will increase when peak-hours of both
streams occur simultaneously. So at the documentation process several streams converge.
Figure 24. Actual truck arrivals at KLM Cargo SPL Hub (export cargo)
Interviews revealed that sporadically, export trucks contain palletised cargo that needs to be unloaded
at one of the MTD doors. Because of this, it is assumed that only transit cargo is unloaded at the MTD
doors and export clients do not unload their cargo at these doors.
With respect to the workforce at freight building 3, management interviewees claimed that unloading
personnel is not dedicated to a certain unloading position. From interviews with operational personnel and
higher management, this suggestion is partly refuted. Personnel on the MTD equipment help others when
no trucks with transit cargo arrive for a certain period of time, but this does not happen the other way
around. Therefore, this research assumes that personnel at freight building 3 is dedicated to one arriving
0,00%
5,00%
10,00%
15,00%
20,00%
25,00%
30,00%
35,00%
Early (>3 hrs) Early (1-3 hrs) Early (0-1 hr) Late (0-1 hr) Late (1-3 hr) Late (>3 hr)
0
5
10
15
20
25
0 4 8 12 16 20 0 4 8 12 16 20 0 4 8 12 16 20 0 4 8 12 16 20 0 4 8 12 16 20 0 4 8 12 16 20 0 4 8 12 16 20
Sun Mon Tue Wed Thu Fri Sat
34
stream when considering the transit cargo stream. For this reason, the export stream will be assumed to be
of no influence on the transit truck stream described herein.
5.3 Truck arrivals to outstations
Another important stream at the SPL Hub is the import stream of cargo into Europe. The arrival pattern
of import trucks is depicted in Figure 25. The main peak-hours of import arrivals are contrary to the export
streams on Sunday evenings and Sunday nights. From field research it is discovered that document clerks
working on transit arrivals, also have to process the arrivals of import trucks. This will be taken into account
when building the model. More elaborate observations were performed and interviews were conducted,
which have led to the assumption that the import stream of cargo is independent from other streams.
Figure 25. Actual truck arrivals at KLM Cargo SPL Hub (import cargo)
5.4 Truck arrivals from local customers (import)
Although the import stream is out of scope, because of the clear separation with the export transit
stream, it is useful to show the time of activity of this stream to substantiate this reasoning with data.
Figure 26 shows the cargo pick-up activity of local customers which is a part of the total import stream.
Every day of the week shows more or less the same arrival pattern, with afternoon peak-hours. This
confirms the separation of streams, because the export streams show peak-hours during the night.
Figure 26. Actual truck arrivals at KLM Cargo SPL Hub (local import cargo)
0
2
4
6
8
1 4 7 10 13 16 19 22 2 5 8 11 14 17 20 23 3 6 9 12 15 18 21 1 4 7 10 13 16 19 22 2 5 8 11 14 17 20 23 3 6 9 12 15 18 21 1 4 7 10 13 16 19 22
Sunday Monday Tuesday Wednesday Thursday Friday Saturday
0,00%
0,20%
0,40%
0,60%
0,80%
1,00%
1,20%
000408121620000408121620000408121620000408121620000408121620000408121620000408121620
Sun Mon Tue Wed Thu Fri Sat
35
Part III
36
6. Experimental design and modelling
The design of a model is an iterative process which starts by formulating the problem of the current
situation (Law, 2007). Data is collected and a model is defined by verifying the made assumptions on the
process. Assumptions should be accepted by all problem owners and should be reasonable to make the
proposed model of any value i.e. creditable. After all the paperwork is done the actual model can be
programmed and verified, whereupon pilot runs can be made. After iteratively verifying and collecting the
right data on the model, design experiments can be created. Once created, production runs can be performed
in order to obtain data on possible future scenarios.
Multiple ways of performing a simulation study exist. The model type and software depends on the
researcher its wishes and the details of the problem that needs to be examined (6.1). The simulation model
needs to match the research scope (6.2) along with feasible assumptions on the process (6.3). A current
state model is created which is used as a baseline for further future designs (6.4). The model structure (6.5)
explains the interaction between multiple sub-processes. To be able to mimic a real-life situation, sub-
processes need the correct parameters which are described in 6.6. Historical data is used as an input to
simulate four months of truck handling at KLM Cargo (6.7). To show the accuracy and credibility of the
model, both verification (6.8) and validation (6.9) methods are used.
6.1 Model type and software
Model type
The used modelling method for this research is simulation. Simulation is a widely used method to model
complex systems. One of the many advantages is the allowance of estimating performance of an existing
system with predefined conditions (Law, 2007). Experimenting with real life cases are often impossible to
perform on a large scale. Alternative system designs can be compared more easily by performing a
simulation study on different key performance indicators set by problem owners. Furthermore,
experimental conditions can be better controlled and maintained during simulation runs. Possible failing of
the simulation modelling process could be a result of: lack of proper system data, wrong level of model
detail and misuse of modelling software.
For this research a discrete event simulation model is used where variables change instantaneously at
different points in time. The handling of the incoming air cargo trucks is comparable to a queueing system
with single or multiple machines. Two high level queues can be distinguished in the SPL Hub handling
process. The first queue in the system appears at the documentation office where a single operator handles
the paperwork of an incoming truck. The second queue is positioned right before unloading at the MTD
doors, which are represented by two processors in the system. The queueing process is discrete, since the
number of waiting entities change instantaneously by an integer number.
37
The problem is modelled by a Mathematical model which is examined by simulation. The used model
is dynamic since it evolves over time. Although the real world is stochastic, for this particular study it is
decided that the model is deterministic, because the sequencing of trucks in the system will not be different
due to stochasticity in any scenario. This means that incidents like a flat tire, machine breakdowns or any
type of disruptions are not taken into account.
Simulation software
To perform the simulation ‘Enterprise Dynamics Developer edition version 8.2.5’ (ED8) is used which
is developed by INCONTROL. The required user license is provided by the TU/e. This modelling tool is
selected because of the earlier successful performed showcases (including at KLM Cargo), visual
presentation opportunities and programmable system pieces. In order to validate the model, it is important
that problem owners with less mathematical background can understand and verify the modelled process.
Using ED8 it is possible to implement mathematical formulas in the background and still give users a visual
representation (2D and 3D) of the system. Discontinuities can be easier detected than just looking at data
and intermediate insights can be found quicker.
A description and a basic model build in ED8 can be found in Appendix H – Enterprise dynamics 8.
6.2 Model properties
6.2.1 System boundaries
The research scope has been defined as and set to “..the export transit flow from the outstations to the
SPL Hub and specifically from departure at the outstation until unloading at the SPL Hub.”. In order to get
results within the scope of this research, the modelled system has to have equal boundaries. The only area
without prior research is the route from the outstations to the SPL Hub. There is no actual control on the
trucks modelled in the system and historical data is used on the arrivals of the trucks. Only at the scenarios
with the highest level of information availability, information on the trucks outside the SPL Hub is used.
For all cases applies that the process starts at the REST procedure, obtaining a visitors pass or at the
documentation office. The system end is set to the exit gate at the SPL Hub.
6.2.2 Model entities
The used entities in the system can be divided in four parts: Truck, Truck driver, Cargo and Shipment.
Not all attributes of the different used entities are taken into account and are for that reason on some points
simplified. However, attributes of the different entities that are necessary to fulfil the research objective,
are used in the model.
The ‘Truck’ entity contains in total four attributes, which are specified below:
38
1. Truck ID – this attribute is used to identify a unique truck arrival and is for that reason a unique
value in the dataset. An example value for this attribute is: ‘KL8502 - 02-02-2017 00’ and is created
by ‘FLIGHT NUMBER – ARRIVAL DATE – ARRIVAL HOUR’;
2. Arrival time – the ‘Arrival time’ attribute is used as the inter arrival period in minutes between two
following trucks. The first truck in the dataset has a inter arrival time of 0 (zero) minutes. All inter
arrival times are non-negative integer numbers;
3. Arrival day – the ‘Arrival day’ of a truck is used to keep hold of the actual arrival day of the truck.
After the simulation it could become hard to exactly find out at which day a truck did arrive. The
value of this attribute is a three letter indication, for example ‘Mon’ to indicate Monday;
4. Security status – the final ‘Truck’ attribute contains information on the security status of the cargo
and is used to decide whether a truck needs to pass the REST procedure or not. This attribute is a
binary number which indicates an unsecure truck by a 1 and secure truck by a 0 (zero).
The second entity is the ‘Truck driver’ with only a single attribute:
1. Access card type – an attribute to indicate if the truck driver needs to get a visitors card in order to
access the hub area. A binary number indicates by a 1 that a truck driver first needs to obtain a
visitor card and by a 0 (zero) if the truck driver already has a valid access card.
Information on the cargo contained by a truck is modelled and described in the ‘Cargo’ entity with the
following two attributes:
1. Number of pallets – this attribute indicates the number of pallets carried by a truck with minimum
of 0 (zero) pallets and a maximum of 4 pallets containing only integer numbers. Trucks with zero
pallets contains only bulk cargo, but are processed alike other trucks only then with a significant
lower process time at the MTD doors;
2. Number of AWBs/shipments – the number of Airway Bills varies from 1 to 65 and are all integer
numbers. The number of AWBs is used for later calculations on the FAP.
The last entity that is used in the model is the ‘Shipments’ entity and has only one modelled attribute:
1. Connection time on arrival – each shipment has a connection time on arrival until departure of the
next flight. The time urgency (heat) of the truck is determined by the shortest connection time of
all shipments on the truck. This attribute can take any (negative) integer number. Negative
connection times indicate that a flight already left on arrival of the shipment at the SPL Hub.
Although three entities are modelled, the actual system only uses one entity with different labels
indicating attributes for all described entities.
39
6.3 Model assumptions
In order to model a real life case, assumptions must be made. For this research project assumptions are
tested on reasonability by performing interviews with stakeholders and problem owners, and observations
on the process. Five areas can be identified where assumptions are made: (1) general: assumptions about
system operations and exceptions, (2) shipments: assumptions on the cargo contained by an arriving truck,
(3) process: assumptions on limitations in the process and process steps, (4) data: assumptions on the
availability of data and integrity of the input data, and (5) scenario specific assumptions: assumptions which
are made for specific scenarios.
The general assumptions are stated below:
Trucks are processed by the given sequencing strategy and do not deviate from the determined
route;
No pre-emption is allowed – i.e. no interruption of the unloading process;
Arrival times vary from the actual schedule. The process can only start after the truck is arrived;
Deadlines are set on the minimum connection time that a shipment has remaing at unloading, failing
this deadline results in a non-FAP;
There are no process down times at any stage in the system;
Independence of other incoming and outgoing truck streams;
KLM Cargo is able to extent the waiting time of incoming transit trucks without direct limitations;
Available entities are processed directly on arrival.
The additional assumptions are listed in Appendix I – Additional model assumptions.
6.4 Experimental setups
In total, 10 scenarios where tested to enhance the FAP. The first scenario is based on the current
handling strategy used at KLM Cargo. This scenario, scenario A, is used as a reference level of performance
to measure the performance increase or decrease of the future designs. As stated earlier in this report, the
scenarios differ on a combination between the level of information that is assumed to be available and the
sequencing strategy that is used.
6.4.1 Sequencing strategies
The most common sequencing strategy First Come First Served (FCFS) handles each arriving truck
based on arrival time, regardless the content of the truck. The deviation in throughput time between the
different trucks will be minimised by usage of this sequencing strategy (Rajendran, 1999). The throughput
40
time for every shipment is more or less the same, because a FCFS strategy will not influence the sequence
based on any information except arrival time.
The sequencing strategy Earliest Due Date (EDD) reduces the maximum lateness of a job (Rajendran,
1999). In this scenario the shipment with the shortest connection time on a truck is used to sequence the
truck in a queue. The average throughput time will increase for trucks, because more trucks will have to
wait on trucks with a higher priority status than theirs.
The Shortest Processing Time (SPT) sequencing strategy reduces the average throughput time by giving
a truck a priority based on the number of pallets carried by that truck. The number of pallets determines the
unloading time which is in this the case the only variable process time. This way of sequencing could lower
the throughput time by a vast amount that could benefit short connecting shipments.
A variant on the algorithm of Moore and referred in this research as Moore’s algorithm, sequences
trucks based on due dates, but ‘removes’ shipments with a connection time lower than the cut-off time
minutes (4 hours + minimal required connection time). These shipments can be considered as already lost
on arrival. This because of the fact that a shipment should be unloaded 4 hours before departure time and
the required throughput time from arrival until unloading. In this case multiple shipment connection times
of a truck load are taken into account. The result of this strategy should minimise the number of tardy
shipments, but the average throughput time will increase. (Moore, 1968)
6.4.2 Information availability
No additional information available: Information on a truck load is obtained from the moment the truck
driver enters the process of cargo documentation checking. The sequencing process will, with this level of
information, start right after the documentation check.
Information on gate entrance: An assumption in this case is that the currently unavailable data from
the license plate scanner, becomes available for KLM Cargo. With this information it is possible to connect
the data from incoming trucks and expected cargo in order to sequence not after the documentation check
but before. Now trucks which are processed at the REST security check or at the security office to get a
visitor pass are taken into account during the sequencing process.
GPS information on truck locations: This level of information requires a significant investment on
technology implementation for KLM Cargo, but is not an uncommon business solution (Artemis, 2015).
Trucks now assumed to be ‘visible’ within a 60 minute range from the SPL Hub. This visibility means
information about: the arrival time, truck load, security status and type of access card. Queueing strategies
are now active before a truck enters the SPL Hub handling area.
41
6.4.3 Scenarios
The different scenarios are stated below:
Scenario A – FCFS with no additional information: Current process handling situation of KLM Cargo.
Scenario B – EDD with no additional information;
Scenario C – SPT with no additional information;
Scenario D – Moore’s algorithm with no additional information;
Scenario E – EDD with information on gate entrance;
Scenario F – SPT with information on gate entrance;
Scenario G – Moore’s algorithm with information on gate entrance;
Scenario H – EDD with GPS information on truck positions;
Scenario I – SPT with GPS information on truck positions;
Scenario J – Moore’s algorithm with GPS information on truck positions.
6.5 Model structure
The chosen level of detail of the model determines the layout of the system its structure. The main
purpose of this research is to investigate the effect of different queueing strategies on the throughput time
of prioritised entities in the system. The level of detail is chosen based on the possibility to make required
changes on future designs. For example, the process steps of the job of a documentation clerk will not be
changed in future designs in this research but the overall process needs to be taken into account in order to
create queue before the documentation check. For that reason, the job of the documentation clerk,
performing a documentation check, is taken as a single process step in the model.
In the case of this model, two effective queues are used: (1) documentation waiting area and (2) parking
spots around the MTD doors. The first queue is connected to a limited multi-processor which imitates a
documentation office clerk. The second queue is connected to two single processors which represent the
unloading process at the MTD doors.
Section 6.5.1 shows the model basics which are used to simulate the arrival and handling process at the
SPL Hub. This model structure is used to perform a simulation run for the first three scenarios.
Since future designs cannot be modelled with exact the same structure, system components need to be
added or removed to reach the desired situation. Section 6.5.2 points out the system alterations which are
required in specific scenarios.
6.5.1 General model structure
The first three scenarios use the same system structure which consists of 21 atoms including additional
atoms to control the desired process. These are the scenarios containing no additional available information
compared to the current situation at the SPL Hub. Broadly the system structure contains:
42
A source imitating entering trucks on the SPL Hub. This is the start of the process;
A multi-server to imitate the REST check for trucks with a positive (one) security status attribute;
A multi-server to imitate the obtainment of a trucker’s visitor pass for truck drivers with a positive
(one) Access card type attribute;
A queue to imitate the waiting area before documentation check;
A multi-server to imitate the documentation check procedure which is required for all truck drivers;
A queue to imitate the parking spots before entering the hub area;
A multi-server to imitate the required driving on the hub terrain from pre-hub parking spots to the
parking spots around the MTD doors;
A queue imitating the parking spots around the MTD doors;
Two single servers to imitate the unloading process of trucks at the MTD doors;
A sink functions as the exit of the SPL hub. This is the end of the process.
More details on the system structure regarding the first three scenarios can be found in Appendix J.1 –
General model structure.
6.5.2 Scenario model structure
Alterations to the general model structure had to be made in order to be able to model several future
design scenarios. Components were added or removed, but never at the expense of losing system logic or
quality. This was possible by adjusting the program code of several other components. A detailed overview
on the made adjustments for the scenarios: D, E, F, G, H, I and J can be found in Appendix J.2 – Scenario
specific model structure.
Algorithms used in Scenarios D, E, F, G, H, I and J all apply information on future events in the system.
The input data gives, amongst other things, information on entity routings through the system. The general
system structure includes, for verification and validation purposes, a process step which represents the
obtainment of a visitors pass. This also holds for the included process step ‘REST’ for securing an unsecured
truck. Because of the made assumptions on the information that is available on trucks, it is possible to take
the just discussed processes out. This simplifies the model to an extent that it is possible to queue trucks
with future information, without losing model quality.
The consequence of this application is that two multi-servers are removed from the general structure
and additional program code is added to other elements of the model.
43
6.6 Model parameters
6.6.1 Current state parameters
The parameters of the model are set according to the assumptions that are made before the modelling
process started. Parameters like capacity, cycle time and queueing discipline are used on model elements
in order to create the desired behaviour. All model parameters used in the current state model (Scenario A)
are listed in Appendix K – Model parameters: current state. An important selection of the parameters is
listed below:
Source – ‘Input’:
(1) Takes the input value listed in the source file as minutes and recalculates this value to
seconds. This value is used as the inter-arrival time between two trucks.
(2) The number of products that will enter the system is set to 8000 based on the input data.
This value rounds the real number of trucks with just a few.
Queue – ‘Incoming truck’:
(1) The capacity of this queue is set to 120. This amount should be sufficient to satisfy the
assumption of unlimited queue size.
(2) The queueing discipline is, in accordance with the current state handle process, First In
First Out.
Multi-server – ‘Visitor’s pass’:
(1) The amount of entities which can be served at once is set to 10. An assumption is made
that the number of security personnel is ‘unlimited’, but the amount is limited to 10. This
should be enough to handle all incoming truck drivers which need a visitor pass.
(2) The cycle time of this process is set to 8 minutes.
Multi-server – ‘REST’:
(1) Also for the REST procedure, an unlimited amount of servers is assumed. For this server
the capacity is set to 10, which covers enough workforce to handle incoming trucks at any
moment.
(2) The cycle time of this process is set to 25 minutes.
Queue – ‘Doc office queue’:
(1) The parking spots before documentation is set to 17, like the real number of parking spots
at the SPL Hub.
44
Multi-server – ‘Documentation check’:
(1) Only one employee is available at the documentation office to handle incoming trucks. The
capacity for this multi-server is set to 1.
(2) The cycle time of this process is set to 8 minutes.
Queue – ‘pre-hub queue’:
(1) The parking spots before documentation are assumed to be no limit to the solutions. The
value for this capacity is set to 17.
Multi-server – ‘Doc office to hub parking’:
(1) The amount of trucks driving from the documentation office to the parking spots in front
of the MTD doors is limited to 1 in order to limit the simultaneous movements.
(2) The cycle time of this process is set to 7 minutes.
Queue – ‘Hub parking’:
(1) The maximum number of available parking spots is limited to 8.
(2) The first truck in the queue will be sent to MTD 1 first if available and otherwise to MTD
2 to unload.
Single processor – ‘MTD 1 / MTD 2’
(1) The setup time is set to 0 minutes.
(2) The cycle time depends on the number of ULDs carried by a truck. 15 minutes is counted
for the process before and after the unloading process. Additionally 3 minutes is added per
ULD. To handle zero ULDs, in the case of only loose cargo, a total processing time of 1
minute is counted.
The parameters are obtained, as shown in earlier sections of this report, by process data analysis,
observations, interviews and prior research done by others.
6.6.2 Future state parameters
Adjustments to codes are available in Appendix L – Model parameters: other scenarios. The main
adjustments are written down in the following sections.
45
6.6.2.1 Scenario B
In Scenario B, the EDD rule is applied to the truck based on the due date of the shipment with the least
remaining connection time on arrival. The parameters differ on one system element, namely the queue right
after the documentation check (Pre-hub queue). The queueing discipline is adjusted to a two-step approach:
Step 1. Index the minimal shipment due date on a truck for all trucks available in the queue;
Step 2. Sort the queue in a non-decreasing order based on the indexed due dates.
6.6.2.2 Scenario C
Scenario C applies a SPT rule, which is based on the number of pallets on a truck. Also for this scenario
holds that only one system element is adjusted which is the ‘Pre-hub queue’. The queueing discipline of
this system element is changed to a two-step approach:
Step 1. Index the number of pallets contained by a truck, for all trucks available in the queue;
Step 2. Sort the queue in a non-decreasing order based on the indexed pallet quantities.
6.6.2.3 Scenario D
A variation on the algorithm of Moore is used in Scenario D. Also for this scenario holds that only one
system element is adjusted which is the ‘Pre-hub queue’. The queueing discipline of this system element is
changed to a four-step approach:
Step 1. Index the minimal shipment due date on a truck for all trucks available in the queue;
Step 2. For all trucks with due dates below cut-off time, if any, take the subsequent shipment on the
truck until the selected shipment exceeds cut-off time;
Step 3. Trucks containing only shipments with less than cut-off time on connection time are set to an
arbitrary due date of 99999;
Step 4. Sort the queue in a non-decreasing order based on the indexed due dates.
These cut-off times are based on: the warehouse cut-off time of 150 minutes before departure,
accumulated by 90 minutes required to break-down and build-up the pallets inside the warehouse and the
minimal required process time from arrival until unloading. This minimal required process time includes:
obtainment of visitors pass, security screening, documentation check, parking time and unloading time.
These times are all truck dependent. Shipments arriving with a smaller connection time than the cut-off time
are therefore seen as already lost shipments. The 90 minutes of warehouse process time is based on Mixed-
pallets, which are assumed to be the only pallets. Through-pallet shipments are still accounted for in later
final FAP calculations.
46
6.6.2.4 Scenario E
The algorithm for this Scenario E becomes a bit more complex due to the future arrival information
that is available. This scenario uses an EDD sequencing strategy, only now with a higher level of
information available. The model parameters used for the REST check and visitors pass obtainment are
now incorporated in the ‘input’ source atom. The sequencing process starts now at entrance of the ‘incoming
trucks’ queue atom.
To control the output of the queue, a condition control system element is used. This is object is
necessary to hold not physically arrived trucks from entering the system and thus remaining in the queue.
The actual arrival time of an incoming truck i is a result of:
ai Queue arrival time of truck i
visti Processing time to obtain an access pass of truck i
resti Processing time for REST procedure for truck i
aai Actual arrival time of truck i
aai = ai + visti + resti
The truck can only leave the queue when:
Time > aai
During the simulation, dummy entities are used to flow through the queue every minute. These
dummies ensure that the statement on the condition control atom is reviewed every minute, otherwise the
statement is reviewed only when new arrivals enter the queue.
When new information becomes available, the following algorithm is used to find the desired position
in the queue:
Step 1. Index the shipment due dates of the trucks already in the queue and arriving truck;
Step 2. Find position i based on sorting on the shipments due dates in a non-decreasing order;
Step 3. Determine completion time if truck is set on position i;
Step 4. Determine completion time if truck is set on position j=i+1;
Step 5. If completion time of arriving truck is greater on position j than position i, finished; else i = j
and return to step 4.
6.6.2.5 Scenario F
The model parameters of Scenario F are quite similar to the model parameters of Scenario E (6.6.2.4).
The main difference is the sequencing strategy that is used, being SPT. Also now the queueing element
‘incoming trucks’ is changed on the queueing discipline which results in the following steps:
Step 1. Index the number of pallets on the trucks already in the queue and on the arriving truck;
Step 2. Find position i based on sorting on the indexed pallet quantities in a non-decreasing order;
47
Step 3. Determine completion time if truck is set on position i;
Step 4. Determine completion time if truck is set on position j=i+1;
Step 5. If completion time of arriving truck is greater on position j than position i, finished; else i = j
and return to step 4.
6.6.2.6 Scenario G
Scenario G is an extension of Scenario D which sequences with the use of a variant on Moore’s
algorithm with limited information available. Now it is assumed that a higher level of information is
available. The structure and model parameters are quite like the parameters outlined in Scenario E (6.6.2.4),
but the queueing discipline differs on the ‘incoming trucks’ element. The algorithm steps are as follows:
Step 1. Index the shipment due dates of the trucks already in the queue and arriving truck;
Step 2. For all trucks with due dates below the cut-off time, if any, take the subsequent shipment on the
truck until the selected shipment exceeds the cut-off time;
Step 3. Trucks containing only shipments with less than cut-off time in minutes on connection time are
set to an arbitrary due date of 99999;
Step 4. Find position i based on sorting on the shipments due dates in a non-decreasing order;
Step 5. Determine completion time if truck is set on position i;
Step 6. Determine completion time if truck is set on position j=i+1;
Step 7. If completion time of arriving truck is greater on position j than position i, finished; else i = j
and return to step 6.
6.6.2.7 Scenario H, I and J
Scenario H, I and J apply the highest level of information available. In this case the condition control
on the output of the ‘incoming trucks’ queue remains, but the actual arrival time of a truck is changed. Now
the actual arrival time is extended with:
𝑖𝑛𝑓𝑜𝑡𝑖𝑚𝑒: 𝑡ℎ𝑒 𝑡𝑖𝑚𝑒 𝑏𝑒𝑡𝑤𝑒𝑒𝑛 𝑎𝑟𝑟𝑖𝑣𝑎𝑙 𝑎𝑡 𝑡ℎ𝑒 𝑔𝑎𝑡𝑒 𝑎𝑛𝑑 𝑐𝑢𝑟𝑟𝑒𝑛𝑡 𝑝𝑜𝑠𝑖𝑡𝑖𝑜𝑛 𝑜𝑓 𝑎 𝑡𝑟𝑢𝑐𝑘
𝑎𝑎𝑖 = 𝑎𝑖 + 𝑣𝑖𝑠𝑡𝑖 + 𝑟𝑒𝑠𝑡𝑖 + 𝑖𝑛𝑓𝑜𝑡𝑖𝑚𝑒
The time period infotime is a constant and does not differ per truck. For all scenarios with this level of
information availability this figure is equal to 60 minutes. In this way it is possible to sequence over a larger
amount of trucks.
Scenario H applies the EDD rule with all algorithm steps like described for Scenario E (6.6.2.4).
Scenario I applies the SPT rule with the algorithm described for Scenario F (6.6.2.5). The final scenario,
Scenario J, applies the variant on the algorithm of Moore as described in 6.6.2.6.
48
6.7 Input data
Recent historical data has been used as an input to run the simulations. Data have been gathered on the
months: February, March, April and May of 2017. After data cleaning, discovered was that in total 8000
trucks arrived during this period, which are all used as model input.
Inter-arrival time: the average inter-arrival time is given in minutes.
ULD pallets: In total, 19312 pallets were delivered to the SPL Hub. On average a truck contained 2.4
pallets.
REST: 4611 trucks were unsecure and need to be screened on arrival. This number is 57 percent of the
total number of trucks.
Visitor pass: 4110 truck drivers needed to get a visitor pass before they could proceed in the process.
This is just over 51 percent of the total number of passing truck drivers.
AWBs / Shipments: A truck load contains on average 9 shipments, which equals to about 72000
shipments during the given period.
Connection times: The connection time of a shipment is sorted ascending, so the shipment with the
least connection time is listed as shipment 1.
Essential to the model output are the input shipment connection times. From historical data, remaining
connection times are presented in Table 3.
Table 3. Remaining shipment connection time on arrival. Distribution of all data points.
Connection time <0 hrs 0 - 4 hrs 4 - 5 hrs 5 - 7 hrs 7 - 10 hrs 10 - 12 hrs >12 hrs
Shipments 1% 1% 1% 5% 14% 10% 68%
6.8 Model verification
6.8.1 External reviews
When writing code for a long period of time, it is possible that the programmer gets into a mental rut
(Law, 2007). To avoid mistakes, during this research project the code is reviewed at least once by an
external party. A walk-through of the sequencing strategy and the produced results gave more insights on
possible modelling errors.
6.8.2 Tracing
The final simulation model, especially for later design scenarios, has become quite complex. At the
start of building the model, the key functions were tested in a small version of the final model. With this
small system representation, it was possible to trace entities through the system. This was done by
49
calculating the output variables by hand and compare these with the actual output variables. Any deviations
from the hand-calculations, assuming these were done right, indicate errors in the model.
ED8 provides a trace window where messages can be projected which are coded in the program. This
is used to show interim values of the entities in the system to check on the system’s validity.
6.8.3 Observing
A main benefit of visual simulation is the power to be able to watch the process go. For example, trucks
stuck longer than usual at a process can be identified easier and a bug in the programming code is found
earlier. This method of verifying the model is used quite some times during the modelling process.
6.9 Model validation
6.9.1 Extreme tests
The model was tested by increasing a few of the input variables to extreme values, to see if it would
function during extreme situations. The changes were made on the number of incoming trucks, amount of
AWBs on one truck, number of pallets on one truck and the range used on the GPS locations of trucks
(Table 4).
Table 4. Model validation: extreme value testing
Input Variable Original value
(OV)
Output
(OV)
Extreme value
(EV)
Output
(EV)
Number of incoming trucks 8000 8000 0 0
Amount of AWBs 1 to 65 1 to 65 0 0
Number of pallets 0 to 4 0 to 4 8 4 (max)
GPS range 0 0 240 240
If no proper inter-arrival times were set in the input data, inter-arrival times were taken as zero. This
resulted in a high amount of trucks. After adjustments on the maximum output on the source, the model
output was zero with zero input.
The number of pallets which can be contained by one truck has a maximum value of 4. A higher input
value than 4 as an input value will be set to 4.
The adjusted GPS range resulted in a longer queue which capacity was insufficient. Therefor the
capacity of the queue was increased.
6.9.2 Real life test
Another way of validating the model is by comparing real life data with the outcomes of the current
state model. The throughput time is a measure which can be used to identify differences between the actual
50
process and the model process. On average, the modelled process takes 5 minutes longer than the actual
handling process at the SPL Hub (77 versus 82 minutes). Looking at a weekday level shows that the
throughput times only differ 2 to 3 minutes on average (Figure 27). Only Tuesday and Saturday show a gap
between the actual throughput time and the modelled throughput time. From interviews is revealed that a
lower value on Saturdays could be a result of two factors: (1) men claim to work harder on shifts which
require a higher workload, and (2) on some days an additional unloading location is used (EHS system)
which is actually meant for truck loading.
Figure 27. Actual throughput time and model throughput time
A second measure which could be used is the FAP value. The model shows a FAP of 78.13 percent.
The actual FAP is around 79.2 percent. So the difference is about 1 percent. Note that the actual FAP is a
calculated value also, and is based on the arrival connection time. So deviations are inevitable.
A small difference between the actual process and the modelled process can be neglected, because the
current state modelled process is used as a baseline for further comparisons on future designs.
0
50
100
150
Sun Mon Tue Wed Thu Fri Sat Weekaverage
TPT
(min
.)
Actual TPT Model TPT
51
7. Results
The way KLM Cargo handles trucks today is simulated in a high level current state model. Four months
of historical data is used as input to this model, which leads to the results as described in 7.1. These baseline
results are used to compare results of future designs on the way how trucks potentially could be handled in
the future (7.2). The used input data and simulation outputs only have a predictable value to a certain extent,
because currently running projects will influence the supply chain. To be able to show the effects of possible
changes to the data, a sensitivity analysis is used (7.3).
7.1 Current state model results
Scenario A, the modelled current state at KLM Cargo, functions as a baseline to compare future designs.
Results on the throughput time of the model show similarity with the earlier described current performance
of the process (Figure 28). Trucks on Monday require the least throughput time on average, with a slight
increase for the following weekdays. The weekends show the recognisable pattern of high waiting times.
Figure 28. Average throughput time of the current state model
7.1.1 FAP calculation - option A
Earlier performed data analysis resulted in a piecewise function (Equation 1), fitted by using a non-
linear least squares method. This function is depicted in Figure 29. A lower limit is shown at 150 minutes
of connection time, because the ULD has to be ready for transport by then. From this point, a steady increase
in FAP probability can be noted until approximately 600 minutes before departure. Values greater than 600
minutes of connection time, do not influence the FAP probability due to inefficient warehouse processes.
This limits the FAP probability at around 83 percent.
0
20
40
60
80
100
120
140
Sun Mon Tue Wed Thu Fri Sat
TPT
(min
.)
52
Figure 29. Fitted FAP equation of the current state
Using Equation 1, the current state model results in an average FAP value of 78.13 percent. Since the
number of truck arrivals differ a lot depending on the day of the week, it is useful to show difference in
FAP values a daily basis.
Figure 30. Average FAP results (day-to-day) of the current state model
Figure 30 shows varying results in respect of the average daily FAP values, where Tuesdays have the
highest FAP values and Sundays the lowest. The decrease in FAP values during the weekends can be
explained by the high throughput times due to high waiting times. Only the Mondays show a conspicuous
number. In this case the throughput time is quite low, which should result in high FAP values, but in reality
the FAP values are relatively low when compared to other weekdays. A possible explanation for this
number is that only 3 percent of all shipments arrive on Mondays, while on Saturdays 25 percent of all
shipments arrive. However, the number of shipments with low connection times is relatively high on
Mondays.
7.1.2 FAP calculation - option B
Using Equation 1 is one way to calculate the FAP value. An alternate approach is to set a minimum
value on the connection time of a shipment that has to be left before departure. This value would be the
result of the time needed for warehouse processing (90 minutes) and the cut-off time that is demanded by
0,00%
20,00%
40,00%
60,00%
80,00%
100,00%
0 100 200 300 400 500 600 700
FAP
pro
b.
Connection time at unloading
Equation 1
70,00%
72,00%
74,00%
76,00%
78,00%
80,00%
82,00%
Monday Tuesday Wednesday Thursday Friday Saturday Sunday
FAP
53
flight operations (150 minutes), which results in 240 minutes. Shipments with more than 240 minutes of
connection time remaining at unloading, are assumed to be FAP and less than 240 minutes as losses.
Figure 31 shows all losses during the week, which totals at a number of 3062 losses (± 96% of all
shipments are unloaded on time). Among these losses are also losses which had zero connection time left
at arrival and could not be recovered in any way. These shipments can as such not be classified as potentials
for improvement.
Figure 31. All shipment losses with a cut-off time of 240 minutes at unloading
Shipments with more than 5 hours (300 min) on connection time left at arrival, are shipments which
have a high potential to be ‘saved’ by using sequencing strategies. A total of 1105 lost shipments have a
connection time of more than 5 hours left on arrival and have less than the cut-off time remaining at
unloading (Figure 32). The losses are mainly concentrated on Saturday morning and Sunday morning.
Figure 32. Shipment losses with a cut-off time of 240 minutes at unloading and more than 5 hours remaining on arrival
The amount of shipments that have a remaining connection time of 4 hours (240 minutes) on arrival,
and do not have a remaining connection time of 4 hours on unloading is equal to 1726. This is 56 percent
of all lost shipments and 2.4 percent of all 71654 shipments. If the minimum required processing time of
16 minutes (documentation check, driving and no pallets) is added to the 240 minutes, so a total of 256
minutes on arrival, a total of 1583 shipments remain. This is an indication of the maximum potential of
shipments which can be ‘saved’ by using sequencing strategies.
0
50
100
150
200
Mo
nd
ay 0
0
Mo
nd
ay 0
4
Mo
nd
ay 0
8
Mo
nd
ay 1
2
Mo
nd
ay 1
6
Mo
nd
ay 2
3
Tues
day
03
Tues
day
07
Tues
day
11
Tues
day
15
Tues
day
19
Tues
day
23
Wed
nes
day
03
Wed
nes
day
07
Wed
nes
day
11
Wed
nes
day
15
Wed
nes
day
19
Wed
nes
day
23
Thu
rsd
ay 0
3
Thu
rsd
ay 0
7
Thu
rsd
ay 1
1
Thu
rsd
ay 1
5
Thu
rsd
ay 1
9
Thu
rsd
ay 2
3
Frid
ay 0
3
Frid
ay 0
7
Frid
ay 1
1
Frid
ay 1
5
Frid
ay 1
9
Frid
ay 2
3
Satu
rday
03
Satu
rday
07
Satu
rday
11
Satu
rday
15
Satu
rday
19
Satu
rday
23
Sun
day
03
Sun
day
07
Sun
day
11
Sun
day
15
Sun
day
19
Sun
day
23
0
50
100
150
Mo
nd
ay 0
0
Mo
nd
ay 0
4
Mo
nd
ay 0
8
Mo
nd
ay 1
2
Mo
nd
ay 1
6
Mo
nd
ay 2
3
Tues
day
03
Tues
day
07
Tues
day
11
Tues
day
15
Tues
day
19
Tues
day
23
Wed
nes
day
03
Wed
nes
day
07
Wed
nes
day
11
Wed
nes
day
15
Wed
nes
day
19
Wed
nes
day
23
Thu
rsd
ay 0
3
Thu
rsd
ay 0
7
Thu
rsd
ay 1
1
Thu
rsd
ay 1
5
Thu
rsd
ay 1
9
Thu
rsd
ay 2
3
Frid
ay 0
3
Frid
ay 0
7
Frid
ay 1
1
Frid
ay 1
5
Frid
ay 1
9
Frid
ay 2
3
Satu
rday
03
Satu
rday
07
Satu
rday
11
Satu
rday
15
Satu
rday
19
Satu
rday
23
Sun
day
03
Sun
day
07
Sun
day
11
Sun
day
15
Sun
day
19
Sun
day
23
54
Preliminary conclusions
Results from the current state model indicate that 3062 shipments are not yet unloaded 4 hours (240
minutes) in advance of flight departure time. Approximately two-thirds these shipments have a connection
time of 5 hours or less on arrival. 1105 of the 3062 shipments have a larger connection time than 5 hours,
and are not unloaded 4 hours before departure. Furthermore, are these shipments heavily concentrated
around two or three weeknights. Due to this concentrated arrival of shipments with short connection times,
it will be challenging to create benefits by sequencing. This because of the fact that sequencing creates
additional waiting time on average, which enlarges the total throughput time of a truck.
A maximum of 1583 shipments can benefit truck sequencing strategies, because these shipments have
at least 4 hours of connection time remaining at arrival.
7.2 Future designs
The future process designs at KLM Cargo are based on frequently used job sequencing strategies
combined with a certain level of information that is available on the position of a truck. These future process
designs, scenarios, are modelled and compared to the current state model of the handling process at KLM
Cargo.
A common measure which is used to indicate the performance of a priority rule is the throughput time.
All the model scenario throughput times are shown in Figure 33. A significant difference in throughput
time can be noted between scenarios with no information and scenarios with a higher level of information
available (p<0.01; T = 16.47; n>8,000). A t-test is performed between Scenario B and Scenario E, which
have respectively averages of 87.8 and 113.5, and standard deviations of 64.8 and 124.0. Before the t-test
is performed, an F-test is performed to see if the variances are equal or not. This resulted in an F-value of
3.66, which is higher than the critical value of 1.04. So the variances are unequal. In this case a t-test with
unequal variances is applied.
Scenarios with a SPT sequencing strategy have relatively the lowest average throughput time. This
corresponds to the existing literature on SPT job priority rules (Ramasesh, 1990). An increase in the level
of information increases the throughput time for all sequencing strategies. This is a result of higher waiting
times, due to prioritising of trucks. The EDD rule results in the relatively highest throughput time, because
a larger number of low-connection time shipments are taken into account during the sequencing process
compared to Moore’s algorithm.
55
Figure 33. Average throughput times for all scenarios
7.2.1 FAP calculation - option A
The key results for this research project are the potential FAP increases by using priority rules and a
certain degree of information. In Figure 34 the FAP results are presented for all scenarios. The results vary,
with FCFS as a baseline, between a daily average FAP percentage of -5.8% and +1.79%. The main FAP
differences are on the busy days, Saturday and Sunday (see Appendix M – Model results: FAP per day, for
a more detailed view). This makes sense because of the longer queues on these days which creates potential
to sequence. Remarkable are the relative low FAP values on the quietest day; Monday. This indicates that
a fairly big amount of shipments with a very short connection time arrive on Monday.
Figure 34. FAP for all scenarios
Multiple z-tests are performed on the total proportions of shipments which are indicated as FAP and
non-FAP (Table 5). All differences with the baseline scenario (A) are significant, except of Scenario C
which is not significantly lower than the current state. The proportions (Px) are the average FAP values of
the scenarios (x). The z-test scores are one-tailed and indicate whether a scenario has a higher/lower FAP
value than the baseline scenario, Scenario A.
87,0 87,8 85,4 87,7
113,5102,3
112,7126,1
114,8124,9
0
20
40
60
80
100
120
140TP
T (m
in.)
FCFS - no info (A)EDD - no info (B)SPT - no info (C)Moore - no info (D)EDD - gate info (E)SPT - gate info (F)Moore - gate info (G)EDD - GPS info (H)SPT - GPS info (I)
68,00%
70,00%
72,00%
74,00%
76,00%78,00%
80,00%
Monday Tuesday Wednesday Thursday Friday Saturday Sunday Average
FAP
FCFS - no info (A) EDD - no info (B) SPT - no info (C) Moore - no info (D) EDD - gate info (E)
SPT - gate info (F) Moore - gate info (G) EDD - GPS info (H) SPT - GPS info (I) Moore - GPS info (J)
56
Table 5. Z-test results on FAP proportions of future designs (scenarios) compared to the current state proportion
Pa- Pb Pa- Pc Pa- Pd Pa- Pe Pa- Pf Pa- Pg Pa- Ph Pa- Pi Pa- Pj
Pa 78.13% 78.13% 78.13% 78.13% 78.13% 78.13% 78.13% 78.13% 78.13%
Px 78.54% 77.82% 78.53% 79.03% 75.60% 79.03% 79.03% 74.31% 79.02%
z-value -1.8837 1.4159 -1.8376 -4.1521 11.3557 -4.1521 -4.1521 16.9831 -4.1056
p-value 0.030 0.078 0.033 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01
All SPT scenarios perform worse than the current state situation, which can be explained by the fact
that this strategy does not include the connection time of a shipment. SPT lowers the average throughput
time of trucks, but this is apparently not that beneficial to the FAP. The FAP value continues to drop as the
level of information increases. An explanation for this is that SPT is not an effective sequencing method in
the first place (for these purposes), which results in prioritising the trucks in the wrong way. Gaining more
information on trucks only amplifies this negative effect.
Due date based job scheduling scenarios, such as Scenario B and Scenario D, increase the FAP value.
FAP increases of 0.41 percent can be noted for both Scenario B and Scenario D, which assume no additional
information on the location of trucks and only get visible to the system after the documentation check.
Scenarios E and G, which assume a higher level of information availability, increase the FAP value by
respectively 0.91 percent and 0.90 percent. This is a considerable increase in relation to Scenarios B and D.
This can be explained by the fact that a higher level of information that is available on the location of trucks,
result in a bigger queue. A bigger queue benefits the sequencing process, because more trucks can be taken
into account and more high priority trucks can be put in front of trucks with lower priorities.
Remarkable is that the highest level of information does not increase the FAP value anymore, so it
stagnates at an increase of 0.90 percent. Two explanations can be given for this stagnation: (1) a higher
throughput time due to increased waiting times and/or (2) the potential is reached. In the first case any FAP
benefits gained for one truck, results in more waiting time for another. This waiting time results in a lower
FAP value. In the second case, the potential is reached within the scope of this research. Although the
potential FAP increase is calculated before (Appendix E – Calculation increase in FAP), which equalled
1.6 percent, the practical FAP increase potential could be lower. The reason for this is that the previously
performed calculation did not take increased waiting times into account for other trucks, which would result
in a lower overall FAP value.
57
7.2.2 FAP calculation - option B
As explained in Section 7.1, a deadline on the final unloading time for a shipment may be imposed.
This value should be set in that case to 4 hours before departure. Every shipment which does not meet that
deadline is counted as a lost shipment.
Figure 35 shows the number of lost shipments for every modelled scenario (the less the better). These
losses include shipments which have less than 5 hours connection time remaining on arrival.
SPT shows an increasing line of losses at an increase on the level of information. As showed in the
previous section, SPT is not an effective sequencing method. Gaining more information on trucks, only
amplifies this negative effect. SPT with the highest level of information more than doubled the number of
lost shipments (6633 vs. 3062).
Scenario J (Moore – GPS info) has the least shipment losses of all scenarios and, given that fact, the
highest performance of all scenarios. Scenario I, SPT – GPS info, scores worst of all scenarios, which is no
surprise considering the objective of the algorithm.
The due date scenarios show the same pattern as shown in the previous section. The lowest levels of
information availability already lowers the number of lost shipments and as the level of information
increases, the number of lost shipments decreases. A difference can be noted between EDD and Moore
based scenarios, where Moore scores better for every level of information availability. This can be
explained by the fact that Moore’s algorithm takes the minimum processing time on top of the 240 minute
deadline, while EDD algorithms also take already lost shipments on arrival during the sequencing process
into account.
The alternate FAP calculation shows a similar a stagnation of the decrease of lost shipments. Scenario
J lowers the amount of lost shipments by 1042. This is very close to the 1105 high potential shipments,
which were lost during the current state handling process and had more than 5 hours of connection time left
on arrival. So one could state that 94 percent of all high potential shipments were saved by making use of
Moore’s algorithm and the highest level of location information availability.
Figure 35. Lost shipments with unloading cut-off time
3062 26503361
26492104
5430
2056 2074
6633
2020
01000200030004000500060007000
FCFS - noinfo (A)
EDD - noinfo (B)
SPT - noinfo (C)
Moore -no info (D)
EDD - gateinfo (E)
SPT - gateinfo (F)
Moore -gate info
(G)
EDD - GPSinfo (H)
SPT - GPSinfo (I)
Moore -GPS info
(J)
Lost
sh
ipm
ents
58
On average, more than 95 percent of all shipments are unloaded 4 hours before departure (Figure 36),
except of scenarios which are SPT based. The earlier trend can be recognised again, where the SPT
scenarios lose relatively and absolutely the most shipments. The power of this figure is the indication of the
potential which can be reached if further processes in the supply chain increase their efficiency. Of course
this decrease is not only due to the processes, but could also be a result of outdated transit times which are
used now.
Figure 36. Amount of on-time shipments with unloading cut-off time
7.3 Sensitivity analysis
During this research project, several third party initiatives have been pursued to enhance processes at
two problem areas (warehouse operations and outstation operations) which are out of the scope of this
research, but which will have an effect on the described scenarios from this study. A sensitivity analysis
show the effects of changes to the current operations, on the scenarios and models that are designed.
Section 7.3.1 shows the effect on the FAP values with the assumption of enhanced warehouse
processes. At the end of 2018, a large amount of outstations will use enhanced truck schedules and which
could result in more shipments with less connection time remaining on arrival. For this future scenario, data
input has been changed to show the possible effects (7.3.2).
7.3.1 Future warehouse processes
Due to several factors, it is not possible to get a FAP value of 100 percent. One of the problem owners indicate that the maximum
indicate that the maximum FAP performance is 94 percent due to three causes. 2 percent loss is caused by commercial decision
commercial decision making (overbooked shipments), 2 percent loss is due to internal factors such as aircraft technical failures
aircraft technical failures and 2 percent loss is caused by external factors (weather). The current running projects at the SPL
projects at the SPL Hub are to improve, among others, the handling of transit cargo at the warehouse. A future scenario is given,
future scenario is given, which assumes a higher warehouse performance and a maximum FAP value of 94 percent (
Figure 37).
The current state model results in a FAP value of 78.13%. A future scenario with enhanced warehouse
processes results in an average FAP value of 85 percent, which is a significant increase of almost 7
86,00%
88,00%
90,00%
92,00%
94,00%
96,00%
98,00%
FCFS - noinfo (A)
EDD - noinfo (B)
SPT - noinfo (C)
Moore -no info (D)
EDD - gateinfo (E)
SPT - gateinfo (F)
Moore -gate info
(G)
EDD - GPSinfo (H)
SPT - GPSinfo (I)
Moore -GPS info
(J)
Ship
men
ts u
nlo
aded
on
tim
e
59
percentage point (p<0.01; n>50,000) from the current FAP value (Figure 38). This increase is no surprise,
due to the large amount of shipments with high connection times which benefit the increase of the increased
maximum FAP value.
Figure 37. FAP probability values with improved warehouse processes
The used equation for the future performance FAP probability function is shown in Equation 2. As
before, a left bound of 2.5 hours (150 minutes) on the equation is used. To fit the data points of the current
process for shipment connection times smaller than 10 hours (600 minutes), the earlier performed fit is
used. The maximum FAP probability value is 0.94 (94%).
Equation 2. Shipment FAP, with connection time x, with improved warehouse processes
𝑆ℎ𝑖𝑝𝑚𝑒𝑛𝑡 𝐹𝐴𝑃(𝑥) =
{
0 𝒊𝒇 𝒙 < 𝟏𝟓𝟎,−1
249,345𝑥2 +
1
233𝑥 − 0.3254 𝒊𝒇 𝟏𝟓𝟎 < 𝒙 < 𝟔𝟎𝟎,
0.80 +(𝑥 − 600)
1000𝒊𝒇 𝟔𝟎𝟎 < 𝒙 < 𝟕𝟒𝟎,
0.94 𝒊𝒇 𝒙 > 𝟕𝟒𝟎
Using the connection times of the modelled scenarios results in the FAP values shown in Figure 38.
Figure 38. FAP for all scenarios with improved warehouse processes
0%
20%
40%
60%
80%
100%
0 100 200 300 400 500 600 700 800
FAP
pro
b.
FAP (with improved warehouse)
70,00%
75,00%
80,00%
85,00%
90,00%
Monday Tuesday Wednesday Thursday Friday Saturday Sunday Average
FAP
FCFS - no info (A) EDD - no info (B) SPT - no info (C) Moore - no info (D) EDD - gate info (E)
SPT - gate info (F) Moore - gate info (G) EDD - GPS info (H) SPT - GPS info (I) Moore - GPS info (J)
60
On average, significant FAP value improvements are shown for all scenarios compared to the current
situation. This is due to the fact that most shipments have high connection time values on arrival (68%).
These shipments benefit from the increased maximum FAP value of 94 percent. The same improvements
are shown between scenarios (as in the previous section), although it slightly declined. This decrease is a
result of the higher FAP values for cold shipments, which drives the benefits down.
7.3.2 Improved outstation processes
At the outstations, initiatives are being pursued in respect of the truck load and truck schedule. A project
called European Green Fastlanes changes multiple truck loading and scheduling aspects, which could result
in a higher number of shipments with low connection times on arrival at the SPL Hub.
Historical data has been altered which results in a shift of shipment connection times from relatively
high shipment connection times to less connection time on arrival (Figure 39). In this case, a higher amount
(9% vs. 5%) of high potential shipments (5-7 hours of connection time on arrival) are arriving at the SPL
Hub.
Figure 39. Alternative input data due to future outstation processes.
Earlier results show that the SPT priority rule is not beneficial to the FAP. That is why for this part of
the sensitivity analysis, the SPT scenarios are left out.
Figure 40 shows the average FAP values of the different scenarios with the altered input data. On
average, all scenarios taken into account, a significant decrease in FAP value can be noted. The reason for
this is the higher number of shipments with lower connection times, lowers the overall FAP value.
The future scenarios show promising improvements compared to the current state model. At the lowest
information level, Scenario B and Scenario D improve the FAP value by respectively 0.57% and 0.58%.
An increase on the level of information that is available and the improvement is almost doubled (1.10%
and 1.14%). A further increase in information results in a decline in FAP values. This is caused by the
higher waiting times for other trucks, certainly in the case of EDD which focusses less on the higher
potential shipments.
0%
20%
40%
60%
80%
<0 hrs 0-4 hrs 5-7 hrs 7-10 hrs 10-12 hrs >12 hrs
historical data future data
61
Figure 40. Average FAP values (sensitivity analysis: outstations)
A detailed view on the FAP values on a weekday level is shown in Figure 41. The values are calculated
by using Equation 1. The highest improvements in FAP are made during the weekends, where a maximum
improvement of 2.31% is gained by Scenario G (Moore – Gate Info) on Saturdays and a maximum FAP
improvement of 2.12% is gained by Scenario J (Moore – GPS Info) on Sundays. The reason why Scenario
G outperforms J on Saturdays could be because of the already high waiting time on Saturdays and an
increase in level of information would only increase the waiting time more. This is detrimental to other
shipments.
Figure 41. FAP values on a weekday level (sensitivity analysis: outstations)
Results from the alternative calculation method also show significant changes in the number of lost
shipments (Figure 42). An alternative calculation method is maintaining a deadline of 4 hours on shipment
connection time upon unloading, where losses are defined as shipments which do not meet this deadline.
Using the current state model results in a total of 3709 lost shipments, being an increase compared to
the original 3062 lost shipments. Similar to the other case, a vast amount of these lost shipments are
unloaded on time due to the use of effective sequencing strategies. Again, an increase in information
availability seems to benefit Moore’s algorithm and EDD based scenarios to a certain extent.
75,73%
76,30% 76,31%
76,83% 76,87%76,74% 76,84%
75,00%
75,50%
76,00%
76,50%
77,00%
FCFS - no info(A)
EDD - no info(B)
Moore - noinfo (D)
EDD - gate info(E)
Moore - gateinfo (G)
EDD - GPS info(H)
Moore - GPSinfo (J)
FAP
70,00%72,00%74,00%76,00%78,00%80,00%
Monday Tuesday Wednesday Thursday Friday Saturday Sunday
FAP
FCFS - no info (A) EDD - no info (B) Moore - no info (D) EDD - gate info (E)
Moore - gate info (G) EDD - GPS info (H) Moore - GPS info (J)
62
Figure 42. Lost shipments (sensitivity analysis: outstations)
7.4 Business implications
An increase in FAP could result in a large increase in revenue. One percentage point of FAP increase
should result in a yearly 3 million euros revenue increase (based on internal sources). The reasoning behind
this is a comparison between the business performances of KLM Cargo and Air France Cargo. A higher
service level will lead to customers becoming more accepting of a higher price. The calculation is based on
the yield from both parties and the difference in Delivered as Promised performance. The Delivered as
Promised is suggested to be highly correlated to the FAP.
The results of the future design scenarios, which have a job scheduling strategy based on due dates,
show an increase in FAP value. These results can be translated to financial numbers as is shown in Table
6. A first indication is given on the costs and complexity of the nine scenarios, as a business implementation
does unfortunately not only imply revenues. The nine different future scenarios each require a certain
amount of information that is available and a way to control truck streams at the SPL Hub. The main
investment costs can be broken down into the information system infrastructure and employee costs for
implementing the new system. Complexity involves the number of stakeholders that need to be taken into
account, size of required system links and the number of persons that need to be informed.
Scenarios B, C and D, each only require small system adjustments, since the information is already
available on the trucks at that stage and only needs to be presented in the right way. For example by putting
an LCD-screen inside of the documentation office to indicate the sequencing of trucks. The first three
scenarios can for that reason be seen as relatively inexpensive and easy to implement.
Scenarios that assume truck information becomes available upon gate entrance require more system
adjustments since the required system links are not yet in place, although the IT hardware is present.
Customs already scan license plates of trucks before entrance. These scans are currently not shared with
KLM Cargo. The complexity of these scenarios relates to the obtainment of a secure communication line
between Dutch Customs and KLM Cargo and the willingness of governmental parties.
3709
2933 2909
2345 2208 24132149
0
1000
2000
3000
4000
FCFS - no info(A)
EDD - no info(B)
Moore - no info(D)
EDD - gate info(E)
Moore - gateinfo (G)
EDD - GPS info(H)
Moore - GPSinfo (J)
Lost
sh
ipm
ents
63
The last three scenarios assume the availability of real-time data on truck positions, even outside the
SPL Hub’s terrain. These scenarios most likely require involvement of third-party IT developers. Next to
the development costs, a GPS transmitter is required on every truck. The complexity of these scenarios lies
in the involvement of all trucking companies (15+) which need to participate in order to get all expected
benefits.
Table 6. Business implications for implementing the studied scenarios
Scenario B C D E F G H I J
FAP increase 0.41% -0.31% 0.40% 0.90% -2.53% 0.90% 0.90% -3.82% 0.89%
Rev. (M€) 1.23 -0.93 1.2 2.7 -7.59 2.7 2.7 -11.46 2.67
Costs + + + + + + +++ +++ +++
Complexity + + + ++ ++ ++ +++ +++ +++
The sensitivity analysis showed a significant increase of the FAP value when warehouse processes are
assumed to be optimised. Even the current truck handling procedures would result in a 7 percentage point
FAP increase. A quick and simple calculation would conclude a potential increase in revenues of € 21M.
Although the potential benefits of an improved warehouse are huge, one should not underestimate the
complexity of the SPL Hub’s warehouse and with that the complexity of possible solutions.
Another result from the sensitivity analysis is the relative increase of the FAP by using the sequencing
strategies in a case of a higher percentage short connecting shipments. Increases of more than 1.1 percent
FAP are obtained which can be quantified as € 3M of revenue increase each year. This scenario would only
strengthen the business case of the implementation of a dock & yard management system. However, this
scenario would also result in a strong decline in the overall FAP by approximately 2 percent.
64
Part IV
65
8. Discussion and conclusions
It can be concluded that KLM Cargo is currently underperforming for a number of reasons, which can
be located in three main problem areas. The first problem area relates to the poorly performing processes
at the outstations which function as the outer points of the hub-and-spoke model. Another problem area
concerns the processes at the hub’s warehouse, which is affected by the recently finalised reorganisation.
The area that remains underexposed may be formulated as the air cargo stream from the outstations towards
the SPL Hub by truck. Although this problem area historically received little research attention at KLM
Cargo, senior management argued that the disappointing hub performance is partly due to a lack of control
on the truck transit stream until unloading. The possible gain in throughput time by prioritising trucks would
be approximately one hour, based on the difference between the minimum throughput time and the average
throughput time. This research project has focused on the possibilities and opportunities arising when KLM
Cargo is able to control trucks on the hub and when KLM Cargo is able to anticipate on future arriving
trucks.
Four frequently applied job scheduling strategies are used in order to increase the overall FAP value. It
can be concluded upon the research results that the use of these strategies with additional information
available, result in a significant higher throughput time. Each successive step in level of information comes
with an increase of the average throughput time of 10 to 30 percent. The weekends show a recognisable
pattern of long waiting times, which lead to a higher throughput time. SPT based scenarios show the lowest
average throughput, while EDD based scenarios result in the highest average throughput time.
4 months of historical data is used as an input which results in an average FAP value of 78.13 in the
case of the current state handling method at KLM Cargo. The daily FAP averages vary over the week in
which Tuesdays show the highest values and Sundays the lowest. More than 4 percent of all shipments are
not unloaded 4 hours before departure, with one half of this percentage of all arriving shipments having
less than 4 hours connection time remaining before the departure, and are therefore considered to be lost
on arrival. The other half, the remaining 2.4 percent of all shipments which are lost shipments in the current
state consists of potential shipments which could be unloaded on time by using sequencing strategies. The
latter half includes shipments with a very short connection time on arrival of 4 to 5 hours (0.9 percent of all
shipments). 1.5 percent of all shipments, equal to 1105 shipments of the total amount of 72,000 shipments
in the used data set, are high potential shipments for this project.
Future design scenarios show clear differences between process time based algorithms and due date
based algorithms. The effect size of the strategies depends on the weekday. The greatest benefits can be
obtained on Saturdays and Sundays, because of the high truck arrival rate and the congestion that comes
66
with it. All due date based algorithm show FAP improvements up to 1.79 percent on these busy days like
Saturdays and Sundays, while the other scenarios cause a decline of the FAP value of several percentages.
The overall FAP value is improved by +0.41% for both Scenario B (EDD – no info) and Scenario D
(Moore – no info), +0.91% and +0.90% for respectively Scenario E (EDD – gate info) and Scenario G
(Moore – gate info), and finally +0.90% for both Scenario H (EDD – GPS info) and Scenario J (Moore –
GPS info). It can be concluded that the level of information does influence the improvement of the FAP
value, but stagnates at a certain point.
Considering the number of shipments that are unloaded on time (4 hours before departure) as a FAP
measure, will result in a 95 percent score for the current state model. Improvements are shown for all due
date bases scenarios. As the level of information increases, the number of lost shipments decreases. This
holds for all scenarios except of the SPT based scenarios. Scenario J (Moore – GPS info) outperforms all
other scenarios and lowers the total amount of lost shipments by 1042, which is equal to saving 94% of all
high potential lost shipments.
A future scenario with enhanced warehouse processes results in an average FAP value of 85 percent,
which is a significant increase of almost 7 percentage point from the current FAP value. In a possible future
scenario which implicates a higher number of high potential shipments (9% vs. 5%), the overall FAP value
will drop by 2.5%. Nevertheless, the sequencing strategies will improve relatively more than with the non-
altered data set. The weekends will again gain the most benefit from the sequencing strategies with FAP
improvements up to 2.31 percent.
The business implications for the future designs result in a revenue increase of € 1.2M for the first two
due date based scenarios. An increase on the level of information that is available results in a revenue
increase of € 2.7M yearly.
The overall conclusion on this report is that EDD and Moore’s algorithm sequencing strategies are
beneficial for the FAP value, especially during peak-hours. An increase of the level of information amplifies
this positive effect to a certain extent due to the increasing waiting time that is involved with the level of
information. Although the amount of high potential shipments is limited, because the vast majority of the
cargo has a large connection time on arrival, it is showed that sequencing strategies are able to save 94%
of high potential lost shipments.
Advised to KLM Cargo is, based on the model results from this research project, to perform a follow-
up study on the financial consequences that are involved on the implementation of a truck sequencing
system. With that, it is advised to start investigate Scenario B (EDD – no info) and Scenario D (Moore –
no info) which require the lowest investment costs and are the least complex to implement. Furthermore,
advised is to rerun the model in the future, when process optimisations have been implement at outstations,
and new historical data is available.
67
Additionally, two recommendations can be made based on the insights gained by this research project:
1. It is recommended to KLM Cargo to review the current transit times which are agreed with
customers. As shown in earlier data analysis, an increase in connection time significantly improves
the probability of a shipment’s FAP (quadratic on some points). It could be of great significance to
explore the pros and cons of an increase in the agreed transit time. The pros will concern the
reliability towards the customers (higher FAP), but the customers could dislike the urge to deliver
the cargo earlier.
2. It must be noted that the currently applied transit time of 5 hours on arrival is based on the former
hub layout and processes. From August 2016 the layout of the SPL Hub has been changed and the
REST procedure is added to the process. During interviews, it was revealed that the REST
procedure, in order to secure the freight, was not even accounted for in the first place. So the
additional throughput time of approximately 25 minutes is subtracted from the original 5 hours of
shipment connection time. As shown earlier, the effect of this additional processing time on high
potential shipments could give a significant decrease in FAP probability. The amount of unsecured
trucks is about 50 percent currently. It is recommended to KLM Cargo, if extension of the transit
time is no option, to look for possibilities to secure the freight already at the outstations more often.
Furthermore, it is advised to KLM Cargo to perform further research on a system that is able to control
the flow on the SPL Hub may enhance the FAP value, but could also shift exceptionally high value
shipments forward. These so called ‘monitoring shipments’ could save 1 to 2 hours on throughput time
during peak-hours. This increases the FAP probability by tens of percent for this shipment. Advised to
KLM Cargo is to perform a follow-up study which examines the costs and benefits of the implementation
of such a hub truck control system.
The proposed system will not only enhance the FAP of these shipments, but also increase the safety of
the hub area.
68
Bibliography
Air France - KLM Group. (2017). Our Company - AFKLCARGO. Retrieved March 13, 2017, from
https://www.afklcargo.com/US/en/common/about_us/company_info.jsp
Air France - KLM Group. (2016a). Annual report 2015 - Volume 1 Ambitions. Retrieved 16 March, 2017,
from: http://www.airfranceklm.com/sites/default/files/publications/annual_report_2015-en.pdf
Air France - KLM Group. (2016b). Geschiedenis - KLM Corporate. Retrieved March 13, 2017, from
https://www.klm.com/corporate/nl/about-klm/history/index.html
Ambrosino, D., & Caballini, C. (2014). Congestion and truck service time minimization in a container
terminal. Sun Above the Horizon: Meteoric Rise of the Solar Industry, 5, 1.
ACN. (2016, June 3). Milkrun bestaat 1 jaar. Retrieved June 27, 2017, from http://www.acn.nl/milkrun-
celebrates-its-first-year/?lang=en
Artemis. (2015, July 10). DEMANES PROJECT & WORKSHOP RESULTS. Retrieved June 27, 2017,
from https://artemis-ia.eu/news/demanes-project-workshop-results.html
Baker, K. R., & Trietsch, D. (2013). Principles of sequencing and scheduling. John Wiley & Sons.
Boysen, N., Briskorn, D., & Tschöke, M. (2013). Truck scheduling in cross-docking terminals with fixed
outbound departures. OR spectrum, 35(2), 479-504.
Boysen, N., Fedtke, S., & Weidinger, F. (2017). Truck Scheduling in the Postal Service Industry.
Transportation Science, 51(2), 723-736.
Boysen, N., & Fliedner, M. (2010). Cross dock scheduling: Classification, literature review and research
agenda. Omega, 38(6), 413-422.
Chen, G., & Jiang, L. (2016). Managing customer arrivals with time windows: a case of truck arrivals at
a congested container terminal. Annals of Operations Research, 1-17.
Dekker, R., van der Heide, S., van Asperen, E., & Ypsilantis, P. (2013). A chassis exchange terminal to
reduce truck congestion at container terminals. Flexible Services and Manufacturing
Journal, 25(4), 528-542.
Feng, B., Li, Y., & Shen, Z. J. M. (2015). Air cargo operations: Literature review and comparison with
practices. Transportation Research Part C: Emerging Technologies, 56, 263-280.
Guan, C. Q. (2009). Analysis of marine container terminal gate congestion, truck waiting cost, and
system optimization. NEW JERSEY INSTITUTE OF TECHNOLOGY.
Hall, R. W. (2001). Truck scheduling for ground to air connectivity. Journal of Air Transport
Management, 7(6), 331-338.
IATA. (2017). Air Cargo Industry Master Operating Plan. Retrieved 16 March, 2017, from:
http://www.iata.org/whatwedo/cargo/cargoiq/Documents/cargoiq-industry-mop.pdf
69
INCONTROL (n.d.). The Debugger [PDF file]. Retrieved from
http://community.incontrolsim.com/download/file.php?id=2&sid=c6c5f410fea2b7aaedce687bc
5390d11
Konur, D., & Golias, M. M. (2013). Analysis of different approaches to cross-dock truck scheduling with
truck arrival time uncertainty. Computers & Industrial Engineering, 65(4), 663-672.
Law, A. M. (2007). Simulation modeling and analysis.
Lee, C., Huang, H. C., Liu, B., & Xu, Z. (2006). Development of timed Colour Petri net simulation
models for air cargo terminal operations. Computers & industrial engineering, 51(1), 102-110.
Moore, J. M. (1968). An n job, one machine sequencing algorithm for minimizing the number of late
jobs. Management science, 15(1), 102-109.
Morais, P., & Lord, E. (2006). Terminal appointment system study (No. TP 14570E).
Ou, J., Hsu, V. N., & Li, C. L. (2010). Scheduling truck arrivals at an air cargo terminal. Production and
Operations Management, 19(1), 83-97.
Ou, J., Zhou, H., & Li, Z. (2007, September). A simulation study of logistics operations at an air cargo
terminal. In 2007 International Conference on Wireless Communications, Networking and
Mobile Computing (pp. 4403-4407). IEEE.
Pinedo, M. L. (2016). Scheduling: theory, algorithms, and systems. Springer.
Rajendran, C., & Holthaus, O. (1999). A comparative study of dispatching rules in dynamic flowshops
and jobshops. European journal of operational research, 116(1), 156-170.
Ramasesh, R. (1990). Dynamic job shop scheduling: a survey of simulation research. Omega, 18(1), 43-
57.
Saltelli, A. (2002). Sensitivity analysis for importance assessment. Risk analysis, 22(3), 579-590.
Seber, G. A. (2013). Statistical models for proportions and probabilities. Springer.
Van Merrienboer, S. (2015). Real-time smart data platform – Logistiek [PDF file]. Retrieved from
http://www.smartlogisticscentrevenlo.com/files/8357932db59d861827668f0713de667936ff1ec
f/20150205SmartDataPlatform.pdf
Veenstra, A., Zuidwijk, R., & van Asperen, E. (2012). The extended gate concept for container terminals:
Expanding the notion of dry ports. Maritime Economics & Logistics, 14(1), 14-32.
Zehendner, E., & Feillet, D. (2014). Benefits of a truck appointment system on the service quality of
inland transport modes at a multimodal container terminal. European Journal of Operational
Research, 235(2), 461-469.
1
Appendix A – Detailed air cargo process
Appendix A.1 – Forwarding at the origin
A shipment starts with a shipper that wants to transport freight from A to B. The shipper is responsible
for efficient assembly of the shipment to keep the transport costs as low as possible but also to avoid damage
along the way. This shipper sends in a request to a freight forwarder which will take over the responsibility
of arranging the shipping of the freight from origin until destination. The shipper gives several inputs as:
origin/destination, delivery dates, shipment details, shipper/consignee information and the required service
for shipping (IATA, 2016). This is known as a booking.
When the booking is made by a shipper at a forwarder, the shipper’s security status is checked whether
the shipper is a ‘known shipper’ or ‘unknown shipper’ (IATA, 2016). For security reasons, the shipper has
to be known to the forwarder. A shipper has to meet different requirements to become a ‘known shipper’.
After some additional information on custom statuses are received by the forwarder from the shipper,
the forwarder determines the destination airport, routing and selects the air carrier (IATA, 2016). The air
carrier has to confirm the requested capacity and after this is confirmed, the air carrier sends a BKD message
to the forwarder. An Airport-to-Airport Route map is created, which is a tool for supply chain parties to
monitor and control the shipment process (e.g. in Figure 43). A green milestone means that the shipment
has arrived on time or with some offset at that stage. Red indicates that a milestone has failed. For every
milestone, the planned time and the actual time are indicated.
Figure 43. Cargo IQ route map for one shipment showing the successful and unsuccessful milestones
Now that the booking procedure is complete, an appointment is made between the shipper and the
forwarder about the pick-up date and time of the cargo at the shipper (IATA, 2016). The cargo that is
picked-up by the forwarder is brought to the forwarders hub, sometimes via a branch facility, to be prepared
for export. A contract between shipper and freight forwarder is created and is called a House Airway Bill
(HAWB). This is also a proof of receipt between these two parties.
2
Upon arrival (by truck) at the forwarder’s hub a security check is done on the physical integrity of the
truckload (IATA, 2016). Additional security measures could be applied when necessary (e.g. X-ray
screening). After the shipments obtain a validated security status, a Master Airway Bill (MAWB) is created.
A MAWB sets out the contract between the freight forwarder and the air carrier and is a proof of receipt.
Some forwarders do already consolidate and assembly cargo on Unit Loading Devices (ULD), also known
as pallets. Other forwarders deliver cargo without a pallet. Cargo is in that case referred as being ‘loose’
instead of ‘palletized’. Once the cargo is loaded onto the truck, a loading list is created with the actual
shipments that will be transferred to the carrier. Relevant information about the shipment is sent
electronically to the air carrier by a Freight Waybill (FWB). This subpart is illustrated in Figure 44. The
red line indicates the responsibility transition from shipper to forwarder.
The shipment is booked by the
shipper
Shipment is planned by the
forwarder
Shipment is picked-up at the shipper by the
forwarder
Shipment is transferred to
forwarder’s branch facility
BKD
Shipments are sorted & stored and prepared for transport
House Airway Bill (HAWB) is
created
Shipments are transported to the forwarder’s
hub
Shipments are prepared for
export
Master Airway Bill (MAWB) is
created
Shipments are brought to the
airline’s handling agent
by the forwarder
Shipment information is send to the air
carrier/handling agent FWB
Shipment arrives at the
handling agent
SHIPPER FORWARDER
Figure 44. Shipping: from shipper to the forwarder
Appendix A.2 – Air carrier origin activities
The cargo will be transported after all cargo preparations are finalized at the forwarder’s hub, to the air
carrier (IATA, 2016). The party that is responsible for the acceptance of goods is called a handling agent.
This handling agent role is either fulfilled by the air carrier or outsourced to a third party handling company.
The contract between the air carrier and handling agent, determines the level of service provided by the
handling agent.
Upon arrival at the carrier’s domain by truck, the loading list is checked. In some cases when the cargo
is consolidated on a ULD, the manifest of the ULD is checked also.
After the documents are ‘accepted’ by the air carrier, the truck can continue to an unloading dock
(IATA, 2016). The physical acceptance of the cargo gives the status to the shipment of Freight on Hand
(FOH). Unsecured freight has to be screened to obtain the secure status. Any discrepancies on number of
pieces, weight and dimensions are listed and communicated later to the forwarders. Further checks are
performed on the requirements concerning safety, security and customs clearance. When everything is
3
approved the cargo is officially accepted by the air carrier and creates the milestone (RCS). This milestone
could also be presented as the latest acceptance time (LAT).
This subpart is illustrated in Figure 45. The red line indicates the responsibility transition from
forwarder to air carrier.
Shipment arrives from
forwarder’s hub at the air carrier’s
handling agent
Truck is checked on security
status
Truck is unloaded
Shipments are screened and checked on
ready for carriageFOH
Booking and actual delivery of shipments are validated
Handling agent accepts the shipments
RCS
FORWARDER AIR CARRIER
Figure 45. Shipping: from the forwarder to the air carrier
Appendix A.3 – Air carrier transport activities
Now that the cargo is unloaded, it is temporarily stored in the warehouse, brought to the break-down
area when palletized or brought to the build-up area. This warehouse process is coordinated by earlier made
allocations on shipments to flights. A Freight Booked List (FBL) determines which shipments need to be
picked and build upon ULDs at the build-up area for a single destination (IATA, 2016). After all ULDs are
finished for a single flight, a flight manifest is created with information on the weight and size of the ULDs.
The air carrier uses, amongst other documents, this manifest to create the aircraft load plan.
The ULDs are transported to the hold area where they await ramp transportation to the aircraft. When
the ramp transportation deadline is reached, the ULDs are brought to the aircraft parking position. The
cargo is loaded onto the aircraft as described in the final load plan and any discrepancies between the actual
load plan and the transported cargo are listed. Passenger aircrafts can take a relative small amount of cargo
under the passengers in the so called aircraft belly. Another option is transporting by a full freight aircraft
and in this case both the main deck and the belly are used to store cargo. Finally a hybrid of these two
options is air transportation of cargo by a combi aircraft where half the aircraft is filled with passengers and
the other half is filled with cargo.
Once discrepancies are resolved, the flight will depart and another Cargo IQ milestone is created: DEP.
An important KPI of the air carrier is calculated by the achievement or failure of this milestone. This KPI,
which will be explained later on, is used for communication purposes towards the customers on the air
carrier’s performance.
After the flight is departed, all necessary information on the cargo is distributed to the down line stations
and authorities at the destination airport. One of these messages is the Freight Forwarding Message (FFM)
which contains exactly the content of the on board ULDs.
At arrival, an arrival milestone is created: ARR. Now a mirrored process will occur. The cargo is
unloaded from the aircraft and is moved to the warehouse. After the shipments are received at the
4
warehouse, an RCF milestone is created. Transit cargo will be stored in the warehouse and re-enter the
process at the described start of this paragraph.
The forwarder at the destination is being informed by a message that the cargo is ready to be picked-
up at the air carrier’s warehouse. A NFD message is in this case created and a certain time period later the
cargo will be picked-up by the forwarder.
This subpart is illustrated in Figure 46.
Shipments are accepted by the
air carrier’s handling agent
Shipments are sorted, stored and build on
ULDs
ULDs are transported to
the aircraft
ULDs are loaded on the aircraft
Aircraft departs from airport A
Aircraft arrives at airport B
ULDs are transported
from the aircraft to air carrier’s handling agent
DEP
ARR
Freight Forwarding
Message (FFM) is send to the
handling agentFFM
Handling agent receives the
ULDs
RCF
Shipments are sorted and
stored
A message is send to the
forwarder that shipments are ready for pick-
up
NFD
Forwarder arrives at the
handling agent
Figure 46. Shipping: handling agent and air carrier
Appendix A.4 – Forwarding at the destination
The arrived shipments are picked-up and brought by the forwarder to a forwarders hub. The milestone
of delivery is now created (DLV). From this hub the shipments are brought to a branch facility and from
there the shipments are delivered at the consignors. After the Proof of Delivery (POD) status has been
obtained, the cycle is finished.
This subpart is illustrated in Figure 47. The red lines indicate the responsibility transition from air
carrier to forwarder and from forwarder to consignor.
Shipments are ready for pick-up at air carrier’s handling
agent
Shipments are handed over to
forwarder
Shipments are transported to
forwarder’s hub
DLV
Shipments are checked and
discrepancies are resolved
Shipments are transported from
hub to branch facility
Shipments are delivered at the
consignor
POD
AIR CARRIER FORWARDER FORWARDER CONSIGNOR
Figure 47. Shipping: from air carrier to forwarder and from forwarder to the consignor
5
Appendix B – Truck arrival process
Figure 48. Truck arrival process at the SPL Hub. (1) Truck enters the pre-entry area. (2) Truck parks and undergoes if applicable a REST procedure. (3) Truck driver proceeds to
the documentation office to perform a document check. (4) Truck driver returns to truck and enters the hub area. (5) Truck driver parks truck and awaits a free unloading dock. (6)
When the unloading door becomes available, the truck proceeds to the door and starts unloading. (7) The truck exits the hub area after the unloading process has finished.
6
Appendix C – Truck streams at the SPL Hub
Import trucks
Export trucks
Transit trucksREST procedure
Truck parking Freight Building 2Import/Transit
Freight Building 3
MTD doorsExport/Transit
Loose cargo doors
EHS doors
Loose cargo doors
Import/Transit
Documentation
Export/Transit
Import/Export/Transit
Export/Transit
Truck parking
Export/Transit
Truck parking
Import
Freight Building 1
Import/Export
Truck parking
Air Mail - doors
Equation (parcels) -doors
Aerospace parts - doors
Animal hotel - doors
Figure 49. Truck arrival streams at SPL Hub
7
Appendix D – Cause and effect diagram
Low FAP KPICargo did not fly on planned flight
Cargo was not available for platform transportation
Break-down started too late
Not enough workforce
Limited workforce
Inadequate workforce planning
Through pallet was not available on time
Cargo was unloaded too lateTruck arrived too lateat the unloading dock
Limited budget
Truck arrived too lateat the hub
External factorsduring transport
Truck left outstationtoo late
Cargo loadedwhich has too short
connection time
Congestion at the hub
Trucks arrive in peaks
Limited capacity at the hub
Transit trucks are scheduled in peaks
Multiple streams converge at process steps
Flights leave in peaks due to hub and spoke
Pallet was not presented on time
Pallet break-down planning discrepancy
Unexpected work/repairs on pallets
Rescheduled due to priority setting of other cargo
Build-up started too late
Rebooked due to flight capacity constraints
Pallet build-up planning discrepancy
Aircraft payload issue
Pallet was (temporarily) lost in the system
Delayed flight departure
Planned cargo had already too short connection
Flight was cancelledLack of available build-up material
Volume, weight or dimension deviated from planned
Figure 50. Full cause and effect diagram
8
Appendix E – Calculation increase in FAP
Table 7. FAP by increasing connection time of shipments by approximately 1 to 2 hours. Connection time on arrival.
Current FAP
Shipment
connection time
After
STD 0-3 hrs 3-4 hrs 4-5 hrs 5-7 hrs 7-9 hrs
9-12
hrs >12 hrs
FAP prob. 0% 0% 37% 52% 70% 78% 81% 83%
Portion 1% 1% 1% 1% 4% 12% 12% 68%
subtotal 0.0% 0.0% 0.4% 0.5% 2.8% 9.4% 9.7% 56.4%
FAP 79.2%
FAP with one time window increase
Shipment
connection time
After
STD 0-3 hrs 3-4 hrs 4-5 hrs 5-7 hrs 7-9 hrs
9-12
hrs >12 hrs
FAP prob. 0% 37% 52% 70% 78% 81% 83% 83%
Portion 1% 1% 1% 1% 4% 12% 12% 68%
subtotal 0.0% 0.4% 0.5% 0.7% 3.1% 9.7% 10.0% 56.4%
FAP 80.8%
9
Appendix F – Data description and availability
Multiple data sources are used to gain insights on the processes at KLM Cargo. A description on every data
source is given in the following sections. Data was available at least, for all data sources, on the months February,
March, April and May of 2017. These months are also used as an input to the designed simulation model.
SmartLoxs
SmartLoxs is a system which is introduced because of the managerial need for measuring the throughput
times on the SPL Hub’s terrain, to eventually be able to increase the efficiency. Besides this, SmartLoxs is used
to collect data on the trucker’s arrival times to measure on time performance of the different trucking companies.
In consultation with the trucking companies, it is agreed that the SmartLoxs data is leading on the actual arrival
times.
The data from the SmartLoxs system is created by the swipes made by the truck driver’s personal (EU or
ACN) or a visitor card. Every person on the SPL Hub is obliged to visibly wear a card which authorizes this
person to enter the hub’s terrain. The trucker’s card is used at different stages during the process:
1. REST – If applicable, a truck needs to be made secure by a REST check.
a. Card is scanned just before the REST procedure;
b. Card is scanned just after the REST procedure.
2. Documentation – Every truck driver has to pass the documentation office to make sure all applicable
documentation is correct.
a. Card is scanned before entering the documentation office;
b. Card is received by a documentation office employee, after some possible waiting time and scanned on
the SmartLoxs viewer. The truck’s flight number and scheduled arrival date are noted.
3. Gate IN – Truck enters the SPL Hub’s terrain.
a. Card is scanned just before truck entry.
4. MTD unloading – Transit cargo is often palletized and needs to be unloaded on the MTD docks.
a. Just before unloading at the MTD dock for palletized cargo, the card is scanned;
b. After the unloading process is finished, the card is scanned again.
5. Gate OUT – Truck leaves via the exit gate.
a. Card is scanned just before truck leaving the Hub’s terrain.
Cargo IQ
Information on the cargo journey and applicable information on the milestones are stored in the Air France –
KLM Cargo data warehouse. The relevant variables which can be used for this research are listed below:
10
The Airway Bill (AWB) Number;
Information on the origin & destination of the shipment;
Flight date(s);
Cargo type;
Product (segment);
Special Handling Code;
ARR milestone + exception description;
RCS/RCF milestone + exception description;
DEP milestone + exception description.
Cargoal
Customers of Air France – KLM can make online bookings through a booking system called Cargoal.
Information on the shipment contains the following:
The Airway Bill (AWB) Number;
Information on the origin & destination of the shipment;
Flight date(s);
Cargo type;
Size and weight of the shipment; and
Number of package
CHAIN
CHAIN is the operational warehouse management system of KLM Cargo and contains information on the
handling process through the Hub’s warehouse. The available data from CHAIN is collected in KLM Cargo’s
SAP data warehouse. For this research, applicable information is available on:
Arriving flight number;
Arrival time at documentation;
ULD: type, height, weight, volume;
Shipment: AWB number, number of pieces, weight, volume;
Unload location.
CP Workflow
CP Workflow is used as a planning tool for network planners. Network planners are responsible for
scheduling and arranging trucks from outstations. Relevant information that is available on a flight:
Flight number;
11
Flight departure date;
Departure and arrival station;
Required, estimated and actual departure time;
Required, estimated and actual arrival time;
Truck and trailer license plates.
Other data sources
SENSIT parking sensors
The 17 parking spots before the documentation office contain sensors which are able to detect vehicles and so
the occupancy of a parking spot. These sensors are also available at several unloading docks. Further data analysis
should give insights on the possible usability of this data. For example on the occupancy rate and the average
parking duration.
No one uses this system or data at the moment.
License plate scanner
At the entrance of the parking place before documentation, a license plate scanner is available for custom
purposes.
Freight Forwarding Message (FFM)
The FFM, also known as the flight manifest, is sent electronically at departure of the truck from the outstation
and provides information on the on board cargo.
12
Appendix G – Statistical testing (z-test)
Many of the used statistical tests in this research project are proportions based. For example, the number of
shipments which are qualified to be positive Flown as Planned shipments or the number of shipments which are
unloaded on time as part of all shipments.
Because every case involves a larger sample size than 30 and often much larger (n>1000), the Central Limit
Theorem can be used to assume that the data is Normally distributed (Seber, 2013). To test H0 which states that
the first proportion is equal to the second proportion (p1 = p2), a Normal approximation can be used due to the
Central Limit Theorem. The following definitions are used:
pi Population proportion of i
p̂i Sample proportion of i
p̄ Weighted estimate
ni Sample size of i
xi Number of successes in sample i
This results in the following equation to calculate the z0-value:
𝑧0 = (p̂1 − p̂2)
√p̄ ∙ q̄ ⋅ (1𝑛1 +
1𝑛2)
where
p̂𝑖 = 𝑥𝑖𝑛𝑖 , p̄ =
𝑥1 + 𝑥2𝑛1 + 𝑛2
and q̄ = 1 − p̄
Tested:
H0: p1 = p2
H1: p1 ≠ p2
If z0 ≤ -1.96 or z0 ≥ 1.96 a significant difference is found. If z0 ≤ -1.645 or z0 ≥ 1.645 a proportion is
significantly greater or less than the other proportion. In the latter case the equal sign is replaced by a greater-
than or less-than sign.
13
Appendix H – Enterprise dynamics 8
ED8 uses atoms to model the system entities and system components. A basic system representation is shown
in Figure 51. In this example, from left to right, the used system components are:
Product – a system entity, which is depicted by a box/pallet. This atom flows through the system via
the system components.
Source – a system component, indicated by ‘Input’. This atom is used to control the system arrivals.
Queue – a system component, indicated by ‘Queue’. The queue atom holds entities while the
processor is busy and can be used to implement sequencing strategies.
Server – a system component, indicated by ‘Processor’. The server atom represents for example a
machine which manipulates an entity. The entity is hold for a given fixed or variable time and can
become idle and busy. A single processor can hold only one entity at a time and is in this case feed
by the entities from the queue.
Sink – a system component, indicated by ‘Output’. Once the entities are completed they leave the
system via the system’s sink.
Figure 51. Basic queueing system in ED8 with 2D and 3D visual representation
ED8 comes with a large library of system components which are all programmable to the user’s wishes. Other
used atoms during the research will be explained later on in the report. To program all elements, ED8 uses the
programming language 4D Script.
14
Appendix I – Additional model assumptions
Shipments
One truck can carry between 1 and 65 AWBs;
The number of ULDs contained by a truck varies between zero and four;
No rebookings are made on the shipments since the sent FFM;
Only mixed pallets are assumed with equal warehouse process times;
Process
Truck drivers do need an authorisation card in order to proceed in the system;
Drivers of unsecured trucks have always knowledge of the fact that the truck needs to be screened;
Only secured and screened trucks proceed to the documentation office;
All trucks need to pass the documentation office;
No extraordinary handling is required during the unload process;
Trucks carrying only bulk cargo (zero ULDs), are routed likewise;
The number of ULDs decide the variable component of the process time of the trucks;
Every truck first parks and require a fixed setup time before unloading;
After entering the hub area, the truck sequence remains as it is;
The maximum number of available MTD doors is two;
The MTD doors are dedicated and only used by incoming transit trucks;
Only one documentation employee is available to handle incoming trucks;
There are no limitations on parking spots;
There are no limitations on REST parking spots;
Truck driving speeds cannot be influenced;
Obtaining a visitors pass has no resource limitation at the security office.
Data
License plate information is available (on the FFM / booking) and can be obtained by the license plate
scanner;
Shipments on trucks indicated by the FFM are all the shipments a truck is carrying;
Arrival times obtained by SmartLoxs data can be used as actual arrival times.
15
Scenario specific assumptions
Level 3 information – all strategies: information is available on the security status of a truck;
Level 3 information – all strategies: information is available on the requirement of a driver’s access
card;
Level 3 information – all strategies: GPS information is available on all trucks;
Level 3 information – all strategies: Arrival information obtained by the GPS tracker is accurate on the
minute.
16
Appendix J – Model structure
Appendix J.1 – General model structure
Figure 52. General model structure of the ‘no-information’ scenarios. Top picture shows the complete model, middle picture shows the first half (zoomed) and the bottom picture
shows the second half (zoomed).
17
Table 8. Atom connections and description of general model
Atom name Atom type Input channel(s) Central Channel Output channel(s)
Trucks Product (none) (none) Pre-hub entry
Input Source Trucks (none) Incoming trucks
Incoming trucks Queue (1) Input /
(2) One minute
Condition
Control_1
(1) Visitors pass /
(2) Total dummies
Visitors pass Multi server Incoming trucks (none) REST
REST Multi server Visitors pass (none) Doc office queue
Doc office queue Queue REST (none) Documentation check
Documentation check Multi server Doc office queue (none) Pre-hub queue
Pre-hub queue Queue Documentation check Condition
Control_2 Doc office to hub parking
Doc office to hub
parking Multi server Pre-hub queue (none) Hub parking
Hub parking Queue Doc office to hub parking Condition
Control_2
(1) MTD 1 /
(2) MTD 2
MTD 1 Single server Hub parking (none) Gate Out
MTD 2 Single server Hub parking (none) Gate Out
Gate Out Sink (1) MTD 1 /
(2) MTD 2 (none) (none)
Dummies Product (none) (none) One minute
One minute Single server Dummies (none) Incoming trucks
Total dummies Sink Incoming trucks (none) (none)
Condition Control_1 Condition
Control Incoming trucks (none) (none)
Condition Control_2 Condition
Control
(1) Pre-hub queue /
(2) Hub parking (none) (none)
Table Table (none) (none) (none)
Initialize Initialize (none) (none) (none)
Excel ActiveX Excel ActiveX (none) (none) (none)
18
Appendix J.2 – Scenario specific model structure
Scenarios D, E, F, H, I and J – EDD and SPT with Level 2 and Level 3 information
Figure 53. Scenarios D, E, F, H, I and J - model structure. Red outline indicates difference from general model structure.
Table 9. Changes made to atom connections – General structure to Scenarios D, E, F, H, I and J.
Atom name Atom type Input channel(s) Central Channel Output channel(s)
Incoming trucks Queue (1) Input /
(2) one minute Condition Control_1
(1) Documentation check /
(2) total dummies
Documentation check Multi-server Incoming trucks (none) Pre-hub queue
19
Appendix K – Model parameters: current state
Atom name Settings/Triggers 4DScript
Input
Inter-arrival time
(s) mins(ExcelActiveX_Read(output(c)+1,1))
Time till first
product (s) mins(ExcelActiveX_Read(output(c)+1,1))
Number of
products 8000
Send to 1
Trigger on exit
Do(
Dim([InfoTime],vbvalue,mins(0)),
SetLabel([number],output(c),i),
SetLabel([arrivalday],ExcelActiveX_Read(Output(c),2),i),
If(ExcelActiveX_Read(Output(c),6)<0,SetLabel([connectiontime],-1,i),
SetLabel([connectiontime],ExcelActiveX_Read(Output(c),6),i)),
If(ExcelActiveX_Read(Output(c),3)>4,SetLabel([pallets],4,i),
SetLabel([pallets],ExcelActiveX_Read(Output(c),3),i)),
SetLabel([REST],ExcelActiveX_Read(Output(c),4),i),
SetLabel([VIST],ExcelActiveX_Read(Output(c),5),i),
SetLabel([TRUCKID],ExcelActiveX_Read(Output(c),25),i),
SetLabel([AAT],Time+InfoTime,i),
SetLabel([T1b],Time,i)
)
Atom name Settings/Triggers 4DScript
Incoming trucks
Capacity 120
Send to if(Label([dummy], first(c)) = 1,2,1)
Queue discipline content(c)
Input strategy openallic(c)
Trigger on entry 0
Trigger on exit Do(
SetLabel([T1e],Time,i),
20
SetLabel([dT1],Label([T1e],i)-Label([T1b],i),i)
)
Atom name Settings/Triggers 4DScript
Visitors pass
Capacity 10
Cycletime (s) mins(8) *Label([VIST],i)
Input strategy openallic(c)
Send to 1
Trigger on entry SetLabel([T2b],Time,i)
Trigger on exit
Do(
SetLabel([T2e],Time,i),
SetLabel([dT2],Label([T2e],i)-Label([T2b],i),i)
)
Atom name Settings/Triggers 4DScript
REST
Capacity 10
Cycletime (s)
mins(25)*Label([REST],i)
Input strategy openallic(c)
Send to 1
Trigger on entry SetLabel([T3b],Time,i)
Trigger on exit
Do(
SetLabel([T3e],Time,i),
SetLabel([dT3],Label([T3e],i)-Label([T3b],i),i)
)
Atom name Settings/Triggers 4DScript
Doc office queue
Capacity 17
Send to 1
Queue discipline content(c)
Input strategy openallic(c)
21
Trigger on entry 0
Trigger on exit 0
Atom name Settings/Triggers 4DScript
Documentation check
Capacity 1
Cycletime (s) mins(8)
Input strategy openallic(c)
Send to 1
Trigger on entry SetLabel([T4b],Time,i)
Trigger on exit
Do(
SetLabel([T4e],Time,i),
SetLabel([dT4],Label([T4e],i)-Label([T4b],i),i)
)
Atom name Settings/Triggers 4DScript
Pre-hub queue
Capacity 17
Send to 1
Queue discipline content(c)
Input strategy openallic(c)
Trigger on entry 0
Trigger on exit 0
Atom name Settings/Triggers 4DScript
Doc office to hub
parking
Capacity 1
Cycletime (s) mins(7)
Input strategy openallic(c)
Send to 1
22
Trigger on entry 0
Trigger on exit 0
Atom name Settings/Triggers 4DScript
Hub parking
Capacity 8
Send to Min(NrOC(c), NrOC(c) + 1 - IndexMatch(NrOC(c), 1, OCReady(NrOC(c) + 1 -
Count, c)))
Queue discipline content(c)
Input strategy openallic(c)
Trigger on entry 0
Trigger on exit 0
Atom name Settings/Triggers 4DScript
MTD 1
Setup time (s) 0
Cycletime (s) If(Label([pallets],i)=0,mins(1),mins(15)+mins(3)*Label([pallets],i))
Input strategy openallic(c)
Send to 1
Trigger on entry SetLabel([tMTD1b], Time,i)
Trigger on exit
Do(
SetLabel([tMTD1e], Time,i),
SetLabel([tMTD1d], Label([tMTD1e],i)-Label([tMTD1b],i),i)
)
Trigger on end of
setup 0
Atom name Settings/Triggers 4DScript
MTD 2
Setup time (s) 0
Cycletime (s) If(Label([pallets],i)=0,mins(1),mins(15)+mins(3)*Label([pallets],i))
Input strategy openallic(c)
23
Send to 1
Trigger on entry SetLabel([tMTD2b], Time,i)
Trigger on exit
Do(
SetLabel([tMTD2e], Time,i),
SetLabel([tMTD2d], Label([tMTD2e],i)-Label([tMTD2b],i),i)
)
Trigger on end of
setup 0
Atom name Settings/Triggers 4DScript
Gate out Trigger on entry
Do(
Cell(input(c)+1,1,Rank(1,Model)) := Label([number],i),
Cell(input(c)+1,2,Rank(1,Model)) := input(c)-Label([number],i),
Cell(input(c)+1,3,Rank(1,Model)) := Label([VIST],i),
Cell(input(c)+1,4,Rank(1,Model)) := Label([REST],i),
Cell(input(c)+1,5,Rank(1,Model)) := (Label([tMTD1d],i)+Label([tMTD2d],i))/60,
Cell(input(c)+1,6,Rank(1,Model)) := (Time-Label([T1b],i)-InfoTime)/60,
Cell(input(c)+1,7,Rank(1,Model)) := Label([connectiontime],i),
Cell(input(c)+1,8,Rank(1,Model)) := Label([connectiontime],i)- ((Time-
Label([T1b],i)-InfoTime)/60),
Cell(input(c)+1,9,Rank(1,Model)) := Label([arrivalday],i),
Cell(input(c)+1,10,Rank(1,Model)) := Label([TRUCKID],i)
)
Atom name Settings/Triggers 4DScript
Dummy
Inter-arrival time
(s) mins(1)
Time till first
product (s) 30
Number of
products -1
Send to 1
Trigger on exit
Do(
SetLabel([AAT],0,i),
SetLabel([dummy],1,i)
)
24
Atom name Settings/Triggers 4DScript
One minute
Setup time (s) 0
Cycletime (s) mins(1)
Input strategy openallic(c)
Send to 1
Trigger on entry 0
Trigger on exit 0
Trigger on end of
setup 0
Atom name Settings/Triggers 4DScript
Total dummies Trigger on entry 0
Atom name Settings/Triggers 4DScript
Condition Control_1
Condition
expression
if(content(in(1,c))<1,1,
if(Label([AAT],first(in(1,c)))>Time,1,0)
)
Flow control on
True 0
User action on
True closeoutput(in(1,c))
User action on
False openoutput(in(1,c))
Atom name Settings/Triggers 4DScript
Condition Control_2
Condition
expression content(in(2,c))>=8
Flow control on
True 0
User action on
True closeoutput(in(1,c))
User action on
False openoutput(in(1,c))
Atom name Settings/Triggers 4DScript
25
Initialize Initialize code
Do(
Cell(1,1,Rank(1,Model)) := [Position],
Cell(1,2,Rank(1,Model)) := [PositionDelta],
Cell(1,3,Rank(1,Model)) := [VIST],
Cell(1,4,Rank(1,Model)) := [REST],
Cell(1,5,Rank(1,Model)) := [MTDtime],
Cell(1,6,Rank(1,Model)) := [TPT],
Cell(1,7,Rank(1,Model)) := [Connectiontime],
Cell(1,8,Rank(1,Model)) := [RCFConnectiontime],
Cell(1,9,Rank(1,Model)) := [ArrivalDay],
Cell(1,10,Rank(1,Model)) := [TRUCKID]
)
Atom name Settings/Triggers 4DScript
Table Rows, Columns 8001, 15
26
Appendix L – Model parameters: other scenarios
Appendix L.1 – Scenario B
Atom name Settings/Triggers 4DScript
Pre-hub queue
Capacity 17
Send to 1
Queue discipline t-findqueuepos([connectiontime],2)
Input strategy openallic(c)
Trigger on entry 0
Trigger on exit 0
Atom name Settings/Triggers 4DScript
Condition Control_2
Condition
expression content(in(2,c))>=4
Flow control on
True 0
User action on
True closeoutput(in(1,c))
User action on
False openoutput(in(1,c))
Appendix L.2 – Scenario C
Atom name Settings/Triggers 4DScript
Pre-hub queue
Capacity 17
Send to 1
Queue discipline t-findqueuepos([pallets],2)
Input strategy openallic(c)
Trigger on entry 0
Trigger on exit 0
27
Atom name Settings/Triggers 4DScript
Condition Control_2
Condition
expression content(in(2,c))>=4
Flow control on
True 0
User action on
True closeoutput(in(1,c))
User action on
False openoutput(in(1,c))
Appendix L.3 – Scenario D
Atom name Settings/Triggers 4DScript
Input
Inter-arrival time
(s) mins(ExcelActiveX_Read(output(c)+1,1))
Time till first
product (s) mins(ExcelActiveX_Read(output(c)+1,1))
Number of
products 8000
Send to 1
Trigger on
creation
Do(
If(ExcelActiveX_Read(Output(c)+1,3)>4,SetLabel([pallets],4,i),
SetLabel([pallets],ExcelActiveX_Read(Output(c)+1,3),i)),
SetLabel([REST],ExcelActiveX_Read(Output(c)+1,4),i),
SetLabel([VIST],ExcelActiveX_Read(Output(c)+1,5),i),
Dim([InfoTime],vbvalue,mins(0)),
Dim([cutOffTime],vbvalue,240 + Label([REST],i)*25 + Label([VIST],i)*8 + 8 +
7 + if(Label([pallets],i)=0,1,15 + Label([pallets],i)*3)),
var([lostAWBs],vbValue,0),
var([ColumnNr],vbValue,6),
var([connectiontime1],vbValue,ExcelActiveX_Read(Input(c),ColumnNr)),
While(AND(connectiontime1<cutOffTime, NOT(connectiontime1=0)),
Do(
Inc(ColumnNr),
connectiontime1 := ExcelActiveX_Read(Input(c),ColumnNr),
Inc(lostAWBs)
28
)
),
SetLabel([LostAWBs],lostAWBs,i),
If(connectiontime1 =0,connectiontime1 :=
ExcelActiveX_Read(Input(c),If(ColumnNr=6,6,(ColumnNr-1)))),
If(connectiontime1<=cutOffTime,connectiontime1:=99999),
SetLabel([conTimeOnArr],connectiontime1 ,i),
SetLabel([connectiontime], Time + InfoTime + mins(connectiontime1), i)
)
Trigger on exit
Do(
Dim([InfoTime],vbvalue,mins(0)),
SetLabel([number],output(c),i),
SetLabel([arrivalday],ExcelActiveX_Read(Output(c),2),i),
SetLabel([TRUCKID],ExcelActiveX_Read(Output(c),33),i),
SetLabel([AAT],Time+InfoTime,i),
SetLabel([T1b],Time,i)
)
Atom name Settings/Triggers 4DScript
Pre-hub queue
Capacity 17
Send to 1
Queue discipline t-findqueuepos([pallets],2)
Input strategy openallic(c)
Trigger on entry 0
Trigger on exit 0
Atom name Settings/Triggers 4DScript
Condition Control_2 Condition
expression content(in(2,c))>=4
29
Flow control on
True 0
User action on
True closeoutput(in(1,c))
User action on
False openoutput(in(1,c))
Appendix L.4 – Scenario E
Atom name Settings/Triggers 4DScript
Input
Inter-arrival time
(s) mins(ExcelActiveX_Read(output(c)+1,1))
Time till first
product (s) mins(ExcelActiveX_Read(output(c)+1,1))
Number of
products 8000
Send to 1
Trigger on
creation Dim([InfoTime],vbvalue,mins(0))
Trigger on exit
Do(
Dim([InfoTime],vbvalue,mins(0)),
SetLabel([number],output(c),i),
SetLabel([arrivalday],ExcelActiveX_Read(Output(c),2),i),
SetLabel([conTimeOnArr],ExcelActiveX_Read(Output(c),6),i),
If(ExcelActiveX_Read(Output(c),6)<0,SetLabel([connectiontime],-1,i),
SetLabel([connectiontime],Time + InfoTime +
mins(ExcelActiveX_Read(Output(c),6)),i)),
If(ExcelActiveX_Read(Output(c),3)>4,SetLabel([pallets],4,i),
SetLabel([pallets],ExcelActiveX_Read(Output(c),3),i)),
SetLabel([REST],ExcelActiveX_Read(Output(c),4),i),
SetLabel([VIST],ExcelActiveX_Read(Output(c),5),i),
SetLabel([TRUCKID],ExcelActiveX_Read(Output(c),33),i),
SetLabel([AAT],Time+InfoTime + mins(8)*Label([VIST],i) +
mins(25)*Label([REST],i),i),
SetLabel([T1b],Time,i)
)
30
Atom
name Settings/Triggers 4DScript
Incoming
trucks
Capacity 120
Send to if(Label([dummy], first(c)) = 1,2,1)
Queue discipline
if(Label([dummy],i)=1,0,
Do(
var([valRank], vbValue),
valRank := (t-findqueuepos([connectiontime],2)),
{CONSTANTS}
var([parking],vbConstant,mins(7)),
var([doccheck],vbConstant,mins(8)),
var([MTD1ready],vbValue,0),
var([MTD2ready],vbValue,0),
var([DOCCHECKready],vbValue,0),
var([valCounter],vbvalue,5),
{Maximum value of finish time MTD1 of trucks in system after queue}
MTD1ready :=
Maximum(7,Label([MTD1out],If(Content(Rank(valCounter+Count,Model))>0,Last(Rank(valCounter+Count,Model)),0))),
{Maximum value of finish time MTD2 of trucks in system after queue}
MTD2ready :=
Maximum(7,Label([MTD2out],If(Content(Rank(valCounter+Count,Model))>0,Last(Rank(valCounter+Count,Model)),0))),
{If no other trucks in queue, calculate process arrival times for incoming truck}
If(Content(c)=1,
Do(
SetLabel([DOCCHECKin],Max(Label([AAT],i),
If(Content(Rank(9,Model))>0,Label([DOCCHECKout],First(Rank(9,Model))))),i),
SetLabel([DOCCHECKout],Label([DOCCHECKin],i)+ doccheck ,i),
SetLabel([PREHUBQin],Label([DOCCHECKout],i),i),
SetLabel([PREHUBQout],Label([DOCCHECKout],i),i),
SetLabel([PARKin],Label([PREHUBQout],i),i),
SetLabel([PARKout],Label([PARKin],i)+parking,i),
SetLabel([HUBQin],Label([PARKout],i),i),
SetLabel([HUBQout],Max(Label([HUBQin],i), Min(MTD1ready,MTD2ready)),i),
If(MTD1ready<=MTD2ready, Do(SetLabel([MTD1in],Label([HUBQout],i),i),
SetLabel([MTD1out],If(Label([pallets],i)=0,Label([HUBQout],i) + mins(1),Label([HUBQout],i) +
(mins(15)+Label([pallets],i)*mins(3))),i)
),
Do(SetLabel([MTD2in],Label([HUBQout],i),i),
SetLabel([MTD2out],If(Label([pallets],i)=0,Label([HUBQout],i) + mins(1),Label([HUBQout],i) +
(mins(15)+Label([pallets],i)*mins(3))),i)
31
))
)
),
{If placed at last position, calculate process arrival times for incoming truck}
If(AND(Content(c)=valRank,Not(Content(c)=1)),
Do(
{Maximum value of finish time MTD1/MTD2 of trucks in queue}
If(valRank>1,
Repeat(valRank-1,
If(Label([MTD1out], Rank(Count,c))>0,
MTD1ready := Label([MTD1out], Rank(Count,c)),
MTD2ready := Label([MTD2out], Rank(Count,c)))
)),
SetLabel([DOCCHECKin],Max(Label([AAT],i), Label([DOCCHECKout],Rank((valRank-1),c))),i),
SetLabel([DOCCHECKout],Label([DOCCHECKin],i)+ doccheck ,i),
SetLabel([PREHUBQin],Label([DOCCHECKout],i),i),
SetLabel([PREHUBQout],Label([DOCCHECKout],i),i),
SetLabel([PARKin],Label([PREHUBQout],i),i),
SetLabel([PARKout],Label([PARKin],i)+parking,i),
SetLabel([HUBQin],Label([PARKout],i),i),
SetLabel([HUBQout],Max(Label([HUBQin],i), Min(MTD1ready,MTD2ready)),i),
If(MTD1ready<=MTD2ready, Do(SetLabel([MTD1in],Label([HUBQout],i),i),
SetLabel([MTD1out],If(Label([pallets],i)=0,Label([HUBQout],i) + mins(1),Label([HUBQout],i) +
(mins(15)+Label([pallets],i)*mins(3))),i)
),
Do(SetLabel([MTD2in],Label([HUBQout],i),i),
SetLabel([MTD2out],If(Label([pallets],i)=0,Label([HUBQout],i) + mins(1),Label([HUBQout],i) +
(mins(15)+Label([pallets],i)*mins(3))),i)
))
)
),
var([TableRef],vbString,Rank(1,Model)),
var([stopLoop],vbvalue,0),
While(AND(NOT(stopLoop),valRank<Content(c)),
Do(
MTD1ready := 0,
MTD2ready := 0,
32
{Maximum value of finish time MTD1 of trucks in system after queue}
MTD1ready :=
Maximum(7,Label([MTD1out],If(Content(Rank(valCounter+Count,Model))>0,Last(Rank(valCounter+Count,Model)),0))),
{Maximum value of finish time MTD2 of trucks in system after queue}
MTD2ready :=
Maximum(7,Label([MTD2out],If(Content(Rank(valCounter+Count,Model))>0,Last(Rank(valCounter+Count,Model)),0))),
{Maximum value of finish time MTD1/MTD2 of trucks in queue}
If(valRank>1,
Repeat(valRank-1,
If(Label([MTD1out], Rank(Count,c))>0,
MTD1ready := Label([MTD1out], Rank(Count,c)),
MTD2ready := Label([MTD2out], Rank(Count,c)))
)),
{Calculate end time for incoming truck without swap}
SetLabel([aDOCCHECKin],Max(Label([AAT],i), If(valRank>1,Label([DOCCHECKout],Rank((valRank-1),c)))),i),
{CHECK IF CURRENT ANALYSED ATOM IS COUNTED}
SetLabel([aDOCCHECKout],Label([aDOCCHECKin],i)+ doccheck ,i),
SetLabel([aPREHUBQin],Label([aDOCCHECKout],i),i),
SetLabel([aPREHUBQout],Label([aDOCCHECKout],i),i),
SetLabel([aPARKin],Label([aPREHUBQout],i),i),
SetLabel([aPARKout],Label([aPARKin],i)+parking,i),
SetLabel([aHUBQin],Label([aPARKout],i),i),
SetLabel([aHUBQout],Max(Label([aHUBQin],i), Min(MTD1ready,MTD2ready)),i),
If(MTD1ready<=MTD2ready, Do(SetLabel([aMTD1in],Label([aHUBQout],i),i), SetLabel([aMTD2out],0,i),
SetLabel([aMTD1out],If(Label([pallets],i)=0,Label([aHUBQout],i) + mins(1),Label([aHUBQout],i) +
(mins(15)+Label([pallets],i)*mins(3))),i),
MTD1ready := Label([aMTD1out],i)
),
Do(SetLabel([aMTD2in],Label([aHUBQout],i),i), SetLabel([aMTD1out],0,i),
SetLabel([aMTD2out],If(Label([pallets],i)=0,Label([aHUBQout],i) + mins(1),Label([aHUBQout],i) +
(mins(15)+Label([pallets],i)*mins(3))),i),
MTD2ready := Label([aMTD2out],i)
)),
SetLabel([finishTimeA],Max(Label([aMTD1out],i),Label([aMTD2out],i)),i),
{Calculate end time for incoming truck with swap}
SetLabel([bDOCCHECKin],Max(Label([AAT],i), Label([DOCCHECKout],Rank(valRank,c))),i), {valRank instead of
valRank-1}
SetLabel([bDOCCHECKout],Label([bDOCCHECKin],i)+ doccheck ,i),
SetLabel([bPREHUBQin],Label([bDOCCHECKout],i),i),
SetLabel([bPREHUBQout],Label([bDOCCHECKout],i),i),
SetLabel([bPARKin],Label([bPREHUBQout],i),i),
SetLabel([bPARKout],Label([bPARKin],i)+parking,i),
33
SetLabel([bHUBQin],Label([bPARKout],i),i),
SetLabel([bHUBQout],Max(Label([bHUBQin],i), Min(MTD1ready,MTD2ready)),i),
If(MTD1ready<=MTD2ready, Do(SetLabel([bMTD1in],Label([bHUBQout],i),i), SetLabel([bMTD2out],0,i),
SetLabel([bMTD1out],If(Label([pallets],i)=0,Label([bHUBQout],i) + mins(1),Label([bHUBQout],i)
+ (mins(15)+Label([pallets],i)*mins(3))),i)
),
Do(SetLabel([bMTD2in],Label([bHUBQout],i),i), SetLabel([bMTD1out],0,i),
SetLabel([bMTD2out],If(Label([pallets],i)=0,Label([bHUBQout],i) + mins(1),Label([bHUBQout],i)
+ (mins(15)+Label([pallets],i)*mins(3))),i)
)),
SetLabel([finishTimeB],Max(Label([bMTD1out],i),Label([bMTD2out],i)),i),
If(Label([finishTimeA],i)<Label([finishTimeB],i), Do(stopLoop := 1, SetLabel([MTD1out],Label([aMTD1out],i),i),
SetLabel([MTD2out],Label([aMTD2out],i),i),
SetLabel([DOCCHECKout],Label([aDOCCHECKout],i))
), Do(inc(valRank), If(NOT(valRank<Content(c)),Do(SetLabel([MTD1out],Label([bMTD1out],i),i),
SetLabel([MTD2out],Label([bMTD2out],i),i),
SetLabel([DOCCHECKout],Label([aDOCCHECKout],i)))))) {NEW}
)),
{END of sequencing process}
{Recalculate values for trucks with higher queue number}
If(Label([MTD1out], i)>0,
MTD1ready := Label([MTD1out], i),
MTD2ready := Label([MTD2out], i)),
If(valRank<Content(c),
Do(
{Recalculate arrival times for truck directly behind truck i}
SetLabel([DOCCHECKin], Max(Label([AAT],Rank(valRank,c)), Label([DOCCHECKout],i)),Rank((valRank),c)),
SetLabel([DOCCHECKout],Label([DOCCHECKin],Rank(valRank,c))+ doccheck ,Rank(valRank,c)),
SetLabel([PREHUBQin],Label([DOCCHECKout],Rank(valRank,c)),Rank(valRank,c)),
SetLabel([PREHUBQout],Label([DOCCHECKout],Rank(valRank,c)),Rank(valRank,c)),
SetLabel([PARKin],Label([PREHUBQout],Rank(valRank,c)),Rank(valRank,c)),
SetLabel([PARKout],Label([PARKin],Rank(valRank,c))+parking,Rank(valRank,c)),
SetLabel([HUBQin],Label([PARKout],Rank(valRank,c)),Rank(valRank,c)),
SetLabel([HUBQout],Max(Label([HUBQin],Rank(valRank,c)), Min(MTD1ready,MTD2ready)),Rank(valRank,c)),
If(MTD1ready<=MTD2ready, Do(SetLabel([MTD1in],Label([HUBQout],Rank(valRank,c)),Rank(valRank,c)),
SetLabel([MTD2out],0,Rank(valRank,c)),
SetLabel([MTD1out],If(Label([pallets],Rank(valRank,c))=0,Label([HUBQout],Rank(valRank,c)) +
mins(1), Label([HUBQout],Rank(valRank,c)) + (mins(15)+Label([pallets],Rank(valRank,c))*mins(3)) ),Rank(valRank,c)),
MTD1ready := Label([MTD1out], Rank(valRank,c))),
Do(SetLabel([MTD2in],Label([HUBQout],Rank(valRank,c)),Rank(valRank,c)),
SetLabel([MTD1out],0,Rank(valRank,c)),
SetLabel([MTD2out],If(Label([pallets],Rank(valRank,c))=0,Label([HUBQout],Rank(valRank,c)) +
mins(1), Label([HUBQout],Rank(valRank,c)) + (mins(15)+Label([pallets],Rank(valRank,c))*mins(3)) ),Rank(valRank,c)),
MTD2ready := Label([MTD2out], Rank(valRank,c))))
34
)
),
var([remainingTrucks],vbValue,valRank+1),
While(remainingTrucks<Content(c),
Do(
var([trucknr],vbvalue,1),
{Recalculate arrival times for trucks behind truck i}
SetLabel([DOCCHECKin], Max(Label([AAT],Rank((remainingTrucks),c)),
Label([DOCCHECKout],Rank((remainingTrucks-1),c))),Rank(remainingTrucks,c)),
SetLabel([DOCCHECKout],Label([DOCCHECKin],Rank((remainingTrucks),c))+ doccheck
,Rank((remainingTrucks),c)),
SetLabel([PREHUBQin],Label([DOCCHECKout],Rank((remainingTrucks),c)),Rank((remainingTrucks),c)),
SetLabel([PREHUBQout],Label([DOCCHECKout],Rank((remainingTrucks),c)),Rank((remainingTrucks),c)),
SetLabel([PARKin],Label([PREHUBQout],Rank((remainingTrucks),c)),Rank((remainingTrucks),c)),
SetLabel([PARKout],Label([PARKin],Rank((remainingTrucks),c))+parking,Rank((remainingTrucks),c)),
SetLabel([HUBQin],Label([PARKout],Rank((remainingTrucks),c)),Rank((remainingTrucks),c)),
SetLabel([HUBQout],Max(Label([HUBQin],Rank((remainingTrucks),c)),
Min(MTD1ready,MTD2ready)),Rank((remainingTrucks),c)),
If(MTD1ready<=MTD2ready,
Do(SetLabel([MTD1in],Label([HUBQout],Rank((remainingTrucks),c)),Rank((remainingTrucks),c)),
SetLabel([MTD2out],0,Rank((remainingTrucks),c)),
SetLabel([MTD1out],If(Label([pallets],Rank((remainingTrucks),c))=0,Label([HUBQout],Rank((remainingTrucks),c)) +
mins(1),Label([HUBQout],Rank((remainingTrucks),c)) +
(mins(15)+Label([pallets],Rank((remainingTrucks),c))*mins(3))),Rank((remainingTrucks),c)),
MTD1ready := Label([MTD1out], Rank(remainingTrucks,c))),
Do(SetLabel([MTD2in],Label([HUBQout],Rank((remainingTrucks),c)),Rank((remainingTrucks),c)),
SetLabel([MTD1out],0,Rank((remainingTrucks),c)),
SetLabel([MTD2out],If(Label([pallets],Rank((remainingTrucks),c))=0,Label([HUBQout],Rank((remainingTrucks),c)) +
mins(1),Label([HUBQout],Rank((remainingTrucks),c)) +
(mins(15)+Label([pallets],Rank((remainingTrucks),c))*mins(3))),Rank((remainingTrucks),c)),
MTD2ready := Label([MTD2out], Rank(remainingTrucks,c)))),
Inc(trucknr),
Inc(remainingTrucks)
)
),
SetLabel([valRankcheck],valRank,i),
valRank {Return position}
))
Input strategy openallic(c)
35
Trigger on entry 0
Trigger on exit
Do(
SetLabel([T1e],Time,i),
SetLabel([dT1],Label([T1e],i)-Label([T1b],i),i))
Appendix L.5 – Scenario F
Atom name Settings/Triggers 4DScript
Input
Inter-arrival time
(s) mins(ExcelActiveX_Read(output(c)+1,1))
Time till first
product (s) mins(ExcelActiveX_Read(output(c)+1,1))
Number of
products 8000
Send to 1
Trigger on
creation Dim([InfoTime],vbvalue,mins(0))
Trigger on exit
Do(
Dim([InfoTime],vbvalue,mins(0)),
SetLabel([number],output(c),i),
SetLabel([arrivalday],ExcelActiveX_Read(Output(c),2),i),
SetLabel([conTimeOnArr],ExcelActiveX_Read(Output(c),6),i),
If(ExcelActiveX_Read(Output(c),6)<0,SetLabel([connectiontime],-1,i),
SetLabel([connectiontime],Time + InfoTime +
mins(ExcelActiveX_Read(Output(c),6)),i)),
If(ExcelActiveX_Read(Output(c),3)>4,SetLabel([pallets],4,i),
SetLabel([pallets],ExcelActiveX_Read(Output(c),3),i)),
SetLabel([REST],ExcelActiveX_Read(Output(c),4),i),
SetLabel([VIST],ExcelActiveX_Read(Output(c),5),i),
SetLabel([TRUCKID],ExcelActiveX_Read(Output(c),33),i),
SetLabel([AAT],Time+InfoTime + mins(8)*Label([VIST],i) +
mins(25)*Label([REST],i),i),
SetLabel([T1b],Time,i)
)
Atom name Settings/Triggers 4DScript
36
Incoming trucks
Capacity 120
Send to if(Label([dummy], first(c)) = 1,2,1)
Queue discipline
SEE SCENARIO E but Replace:
valRank := (t-findqueuepos([connectiontime],2)), by
valRank := (t-findqueuepos([pallets],2)),
Input strategy openallic(c)
Trigger on entry 0
Trigger on exit
Do(
SetLabel([T1e],Time,i),
SetLabel([dT1],Label([T1e],i)-Label([T1b],i),i))
Appendix L.6 – Scenario G
Atom name Settings/Triggers 4DScript
Input
Inter-arrival time
(s) mins(ExcelActiveX_Read(output(c)+1,1))
Time till first
product (s) mins(ExcelActiveX_Read(output(c)+1,1))
Number of
products 8000
Send to 1
Trigger on
creation
Do(
If(ExcelActiveX_Read(Output(c)+1,3)>4,SetLabel([pallets],4,i),
SetLabel([pallets],ExcelActiveX_Read(Output(c)+1,3),i)),
SetLabel([REST],ExcelActiveX_Read(Output(c)+1,4),i),
SetLabel([VIST],ExcelActiveX_Read(Output(c)+1,5),i),
Dim([InfoTime],vbvalue,mins(0)),
Dim([cutOffTime],vbvalue,240 + Label([REST],i)*25 + Label([VIST],i)*8 + 8 +
7 + if(Label([pallets],i)=0,1,15 + Label([pallets],i)*3)),
var([lostAWBs],vbValue,0),
var([ColumnNr],vbValue,6),
var([connectiontime1],vbValue,ExcelActiveX_Read(Input(c),ColumnNr)),
While(AND(connectiontime1<cutOffTime, NOT(connectiontime1=0)),
Do(
Inc(ColumnNr),
37
connectiontime1 := ExcelActiveX_Read(Input(c),ColumnNr),
Inc(lostAWBs)
)
),
SetLabel([LostAWBs],lostAWBs,i),
If(connectiontime1 =0,connectiontime1 :=
ExcelActiveX_Read(Input(c),If(ColumnNr=6,6,(ColumnNr-1)))),
If(connectiontime1<=cutOffTime,connectiontime1:=99999),
SetLabel([conTimeOnArr],connectiontime1 ,i),
SetLabel([connectiontime], Time + InfoTime + mins(connectiontime1), i)
)
Trigger on exit
Do(
Dim([InfoTime],vbvalue,mins(0)),
SetLabel([number],output(c),i),
SetLabel([arrivalday],ExcelActiveX_Read(Output(c),2),i),
SetLabel([TRUCKID],ExcelActiveX_Read(Output(c),33),i),
SetLabel([AAT],Time+InfoTime,i),
SetLabel([T1b],Time,i)
)
Atom name Settings/Triggers 4DScript
Incoming trucks
Capacity 120
Send to if(Label([dummy], first(c)) = 1,2,1)
Queue discipline SEE SCENARIO E
Input strategy openallic(c)
Trigger on entry 0
Trigger on exit
Do(
SetLabel([T1e],Time,i),
SetLabel([dT1],Label([T1e],i)-Label([T1b],i),i))
38
Appendix L.7 – Scenario H
Atom name Settings/Triggers 4DScript
Input
Inter-arrival time
(s) mins(ExcelActiveX_Read(output(c)+1,1))
Time till first
product (s) mins(ExcelActiveX_Read(output(c)+1,1))
Number of
products 8000
Send to 1
Trigger on
creation Dim([InfoTime],vbvalue,mins(60))
Trigger on exit
Do(
Dim([InfoTime],vbvalue,mins(60)),
SetLabel([number],output(c),i),
SetLabel([arrivalday],ExcelActiveX_Read(Output(c),2),i),
SetLabel([conTimeOnArr],ExcelActiveX_Read(Output(c),6),i),
SetLabel([connectiontime],Time + InfoTime +
mins(ExcelActiveX_Read(Output(c),6)),i),
If(ExcelActiveX_Read(Output(c),3)>4,SetLabel([pallets],4,i),
SetLabel([pallets],ExcelActiveX_Read(Output(c),3),i)),
SetLabel([REST],ExcelActiveX_Read(Output(c),4),i),
SetLabel([VIST],ExcelActiveX_Read(Output(c),5),i),
SetLabel([TRUCKID],ExcelActiveX_Read(Output(c),33),i),
SetLabel([AAT],Time+InfoTime + mins(8)*Label([VIST],i) +
mins(25)*Label([REST],i),i),
SetLabel([T1b],Time,i)
)
Atom name Settings/Triggers 4DScript
Incoming trucks
Capacity 120
Send to if(Label([dummy], first(c)) = 1,2,1)
Queue discipline SEE SCENARIO E
Input strategy openallic(c)
39
Trigger on entry 0
Trigger on exit
Do(
SetLabel([T1e],Time,i),
SetLabel([dT1],Label([T1e],i)-Label([T1b],i),i))
Appendix L.8 – Scenario I
Atom name Settings/Triggers 4DScript
Input
Inter-arrival time
(s) mins(ExcelActiveX_Read(output(c)+1,1))
Time till first
product (s) mins(ExcelActiveX_Read(output(c)+1,1))
Number of
products 8000
Send to 1
Trigger on
creation Dim([InfoTime],vbvalue,mins(60))
Trigger on exit
Do(
Dim([InfoTime],vbvalue,mins(60)),
SetLabel([number],output(c),i),
SetLabel([arrivalday],ExcelActiveX_Read(Output(c),2),i),
SetLabel([conTimeOnArr],ExcelActiveX_Read(Output(c),6),i),
SetLabel([connectiontime],Time + InfoTime +
mins(ExcelActiveX_Read(Output(c),6)),i),
If(ExcelActiveX_Read(Output(c),3)>4,SetLabel([pallets],4,i),
SetLabel([pallets],ExcelActiveX_Read(Output(c),3),i)),
SetLabel([REST],ExcelActiveX_Read(Output(c),4),i),
SetLabel([VIST],ExcelActiveX_Read(Output(c),5),i),
SetLabel([TRUCKID],ExcelActiveX_Read(Output(c),33),i),
SetLabel([AAT],Time+InfoTime + mins(8)*Label([VIST],i) +
mins(25)*Label([REST],i),i),
SetLabel([T1b],Time,i)
)
Atom name Settings/Triggers 4DScript
Incoming trucks Capacity 120
40
Send to if(Label([dummy], first(c)) = 1,2,1)
Queue discipline
SEE SCENARIO E but Replace:
valRank := (t-findqueuepos([connectiontime],2)), by
valRank := (t-findqueuepos([pallets],2)),
Input strategy openallic(c)
Trigger on entry 0
Trigger on exit
Do(
SetLabel([T1e],Time,i),
SetLabel([dT1],Label([T1e],i)-Label([T1b],i),i))
Appendix L.9 – Scenario J
Atom name Settings/Triggers 4DScript
Input
Inter-arrival time
(s) mins(ExcelActiveX_Read(output(c)+1,1))
Time till first
product (s) mins(ExcelActiveX_Read(output(c)+1,1))
Number of
products 8000
Send to 1
Trigger on
creation
Do(
If(ExcelActiveX_Read(Output(c)+1,3)>4,SetLabel([pallets],4,i),
SetLabel([pallets],ExcelActiveX_Read(Output(c)+1,3),i)),
SetLabel([REST],ExcelActiveX_Read(Output(c)+1,4),i),
SetLabel([VIST],ExcelActiveX_Read(Output(c)+1,5),i),
Dim([InfoTime],vbvalue,mins(0)),
Dim([cutOffTime],vbvalue,240 + Label([REST],i)*25 + Label([VIST],i)*8 + 8 +
7 + if(Label([pallets],i)=0,1,15 + Label([pallets],i)*3)),
var([lostAWBs],vbValue,0),
var([ColumnNr],vbValue,6),
var([connectiontime1],vbValue,ExcelActiveX_Read(Input(c),ColumnNr)),
While(AND(connectiontime1<cutOffTime, NOT(connectiontime1=0)),
Do(
Inc(ColumnNr),
connectiontime1 := ExcelActiveX_Read(Input(c),ColumnNr),
41
Inc(lostAWBs)
)
),
SetLabel([LostAWBs],lostAWBs,i),
If(connectiontime1 =0,connectiontime1 :=
ExcelActiveX_Read(Input(c),If(ColumnNr=6,6,(ColumnNr-1)))),
If(connectiontime1<=cutOffTime,connectiontime1:=99999),
SetLabel([conTimeOnArr],connectiontime1 ,i),
SetLabel([connectiontime], Time + InfoTime + mins(connectiontime1), i)
)
Trigger on exit
Do(
Dim([InfoTime],vbvalue,mins(0)),
SetLabel([number],output(c),i),
SetLabel([arrivalday],ExcelActiveX_Read(Output(c),2),i),
SetLabel([TRUCKID],ExcelActiveX_Read(Output(c),33),i),
SetLabel([AAT],Time+InfoTime,i),
SetLabel([T1b],Time,i)
)
Atom name Settings/Triggers 4DScript
Incoming trucks
Capacity 120
Send to if(Label([dummy], first(c)) = 1,2,1)
Queue discipline SEE SCENARIO E
Input strategy openallic(c)
Trigger on entry 0
Trigger on exit
Do(
SetLabel([T1e],Time,i),
SetLabel([dT1],Label([T1e],i)-Label([T1b],i),i))
42
Appendix M – Model results: FAP per day
Figure 54. FAP of the different scenarios from day-to-day,
calculated by use of Equation 1.
76,96%76,98%77,00%77,02%77,04%77,06%77,08%
Monday
73,00%74,00%75,00%76,00%77,00%78,00%79,00%80,00%81,00%
Friday
79,00%79,20%79,40%79,60%79,80%80,00%
Tuesday
68,00%70,00%72,00%74,00%76,00%78,00%80,00%
Saturday
74,00%
76,00%
78,00%
80,00%
Wednesday
66,00%68,00%70,00%72,00%74,00%76,00%78,00%
Sunday
70,00%72,00%74,00%76,00%78,00%80,00%
Thursday