a simulation model for capacity planning in sugarcane transport

Computers and Electronics in Agriculture 47 (2005) 85–102

A simulation model for capacity planning insugarcane transport

Andrew Higginsa,∗, Ian Daviesb

a CSIRO Sustainable Ecosystems, Level 3, QBP, 306 Carmody Road,St Lucia 4067, Qld, Australia

b Bundaberg Sugar Limited, PO Box 77, Mourilyan 4858, Qld, Australia

Received 28 May 2003; received in revised form 30 September 2004; accepted 3 October 2004

Abstract

Several mill regions within the Australian sugar industry are currently exploring long-term scenariosto reduce costs in the harvesting and rail transport of sugarcane. These efficiencies can be achievedthrough extending the time window of harvesting, reducing the number of harvesters, and investingin new or upgraded infrastructures. As part of a series of integrated models to conduct the analysis,we developed a capacity planning model for transport to estimate the (1) number of locomotivesand shifts required; (2) the number of bins required; and (3) the delays to harvesting operationsresulting from harvesters waiting for bin deliveries. The model is based on the principles of stochasticsimulation, rather than producing a transport schedule, which gives advantages of flexibility and ease ofapplication. We applied the model in partnership with growing, harvesting and milling representativesat a sugar region in Australia for which one of the scenarios produced by the model was implementedby all harvesters within the region during 2003. The simulation model was also extended to optimiseharvester start times, to explore further opportunities for reducing costs.© 2004 Elsevier B.V. All rights reserved.

Keywords:Sugarcane; Transport; Simulation; Capacity planning

∗ Corresponding author.E-mail addresses:[email protected] (A. Higgins), [email protected] (I. Davies).

0168-1699/$ – see front matter © 2004 Elsevier B.V. All rights reserved.doi:10.1016/j.compag.2004.10.006

86 A. Higgins, I. Davies / Computers and Electronics in Agriculture 47 (2005) 85–102

1. Introduction

In Australia, most sugarcane is grown on the north east coast, where the harvest seasonextends from early Winter to late Spring. This is the time period when the percentage ofextractable sugar from cane (‘commercial cane sugar’, or CCS) is highest. A typical millregion in Australia consists of 200–300 privately owned and operated farms (contained in8–12 geographical districts), covering about 15,000 ha of land under cane, and producingaround 1.5 million tonnes of cane annually. Harvesting machines are usually either privatelyowned and operated by a separate contractor or owned and operated by a grower. There arebetween 15 and 100 harvesters located across a mill region. A harvester will service between1 and 30 farms. These farms are contracted to the harvester and are usually geographicallyclose to one another to reduce the costs of travel between farms. All harvesters operatesimultaneously throughout the harvest season, though some regions have a roster systemto ensure the operators obtain rest days. Each farm usually contains between 20 and 100paddocks, for which a paddock is a single farm field.

As the harvester cuts cane, it fills a bin on wheels hauled by a tractor (namely a hauloutunit) next to the harvester. When full, the haulout unit transports the cane to a nearby railwaysiding. Sidings are short sections of rail track that run parallel to the main rail line, and areused for storing full (wagon) bins and empty bins waiting to be filled. The cane is pouredfrom the tractor hauled bin into the rail wagon bin. When the allotment of empty rail wagonbins has been filled, a locomotive hauls them to the mill. Through negotiation with thesugarcane growers, the harvester operator decides which railway sidings to deliver to eachday of the harvest season, as well as the start and finish time of harvest each day. Thisinformation is then provided to the mill.

About 80% of cane in the Australian sugar industry is transported by rail. Most Australiansugar mills own the narrow-gauge rail network over which the trains operate. Each mill hasa rail network of about 8–12 branch lines, containing a total of up to 200 sidings. In railtransport, rail wagon bins (with a capacity of 4–12 tonnes) carrying sugarcane are hauledan average of 25 km from the sidings to the mill. A siding’s capacity is defined by thenumber of bins it can hold and by the number of harvesters that can use the siding on anyone day. Given the preferred start times and delivery sidings that the harvester operatorsupplies the mill, the cane trains visit these sidings in locomotive runs to pick up full binsor to deliver empty bins. If the mill’s transport system is unable to effectively service theharvesters on some days due to transport capacity restrictions, the mill will negotiate withsome harvester operators to deliver at alternative sidings. In most Australian sugar regions,sugarcane growers cannot have their cane sent to an alternative sugar milling company dueto much longer distances of travel and lack of rail track interconnections.

Sugarcane deteriorates after being harvested and most Australian sugar mills aim to havethe time lapse between when the cane is harvested and processed at the mill (i.e., cut-to-crush time) not exceed 15 h, though the average is about 9 h. A complexity is that in mostAustralian mills, harvesting is conducted in daylight hours only while both the mill andtransport operates continuously. To accommodate daylight harvesting, there is up to 16 hof storage in the bins when waiting for processing at the mill. Harvesting and transportare prone to disruptions from wet weather and breakdowns. A substantial disruption canlead to a lack of cane supply to the mill, resulting in an expensive mill stoppage. Small

A. Higgins, I. Davies / Computers and Electronics in Agriculture 47 (2005) 85–102 87

disruptions are accommodated by the excess capacity from harvesting in daylight hoursonly.

From the scientific and implementation perspectives, optimisation of the full harvestingand transport system is an intractable task. However, exploiting efficiency gains throughbetter integration across these sectors has been regarded as the highest priority in an inde-pendent evaluation of the Australian sugar industry (Hildebrand, 2002). These sectors areseen as having the potential of quick cost reductions over the next 5 years. Developing amore efficient harvesting and transport system raises a number of scientific and change-management issues. There are a large number of logistical and economic linkages anddrivers within the system that need to be identified and measured to identify opportunitiesfor improvements. A modelling framework that encapsulates these drivers was constructed(Higgins et al., 2003) for which a summarised version of the framework is contained inFig. 1. While the main focus in this paper is the transport capacity model, it has been linkedto other existing models of the harvesting and transport systems (seeHiggins (2002)forthe harvester and siding rosters andSandell and Prestwidge (2004)for the harvest haulmodel) to produce whole-of-system scenarios for increased financial gains to the growingand milling sectors of the sugar industry.

Transportation modelling is a large research field in the literature for which most modelsfall into two major categories: (1) models based on scheduled traffic; and (2) models based onunscheduled traffic. While cane transport models are not new (seeAbel et al., 1981; Pinkneyand Everitt, 1997; Milan et al., 2003), almost all existing models are based on scheduledtraffic. A scenario requires the production of an operational transport schedule in order toassess regional characteristics such as cut-to-crush time, bin and locomotive requirements,and the time that harvesters spend waiting for bins. The capacity planning model described in

Fig. 1. Modelling framework of the harvesting and transport system.


this paper is based on unscheduled traffic. While new for sugar transport, transport planningmodels based on unscheduled traffic are commonly used for freight railway applications(e.g.,Higgins and Kozan (1997)for urban planning; andChen and Harker (1990)for single-line freight planning). Models based on producing schedules have advantages over modelsbased on unscheduled traffic in that they provide schedules for operational use, which inturn allows a traffic planner to immediately see how it would work in practice. However,models based on unscheduled traffic have the following advantages over those that produceschedules:

• Better handles scenarios where it is difficult/impractical to produce a schedule. Somestrategic scenarios, such as redesigning a transport track system for use in 5 years time,have a large amount of unknowns needed for an effective application of a schedulingtool.

• Caters for a transport system that has large amounts of unforeseen delay (e.g., locomotivebreakdowns). Models based on producing schedules capture certainty and do not capturethe unforeseen events that lead to increased infrastructure requirements and waiting timefor bins.

• Produces scenarios independent of a schedule. When using a model based on sched-uled traffic, the outputs of locomotive and bin requirements are biased towards thatschedule. That is, if the schedule is changed, so are the outputs. This characteristicmakes it difficult to make fair comparisons between scenarios if their schedules are verydifferent.

• Requires less resources (e.g., input data) compared to models based on scheduled traffic,since less detail is required at the operational level.

• Much less difficult to explicitly integrate with other models in the sugar value chainfor whole-of-system analysis and optimisation, compared to models based on scheduledtraffic. For example, using a transport scheduling tool, it is computationally difficult tosimultaneously optimise the start time combinations of harvesters to achieve the mostefficient utilisation of the transport system.

The capacity planning model was initially developed for the Mourilyan sugar region(80 km south of Cairns, Queensland), and was used to measure the impacts of variousharvesting and transport plans (of a single or multi-year planning horizon) formulated col-lectively by the milling, harvesting and growing representatives. In particular, the modelcalculates the following key attributes across the harvest season: (1) number of locomo-tive shifts and bins required, and (2) the waiting time for harvesters when the capaci-ties in locomotive shifts and bins have been reached. Other attributes such as cut-to-crush time, and siding and marshalling yard utilisations can also be calculated. In thispaper we present the capacity planning model and highlight its capability for mediumto long-term transport planning through application to the Mourilyan case study. Wealso highlight an extension of the model to include an optimisation capability to explorefurther opportunities for cost reductions, namely through optimising the start times ofharvesters.


2. Model description

The capacity planning model for cane transport is based on the principles of stochasticsimulation (Ripley, 1987), for which the following can be summarised from the model out-puts: the number of locomotives and rail bins required; and delays to harvesters. Using themodel, the main variables/scenarios that the milling, harvesting and grower representativeswant to collectively explore are the impacts of changing (from current practice): the num-ber of harvesters and the farms serviced by these harvesters; the start and finish times ofharvesters; the sequence of sidings to be visited by the harvesters; the number and locationof sidings; and improved sidings (i.e., more modern sidings with efficient connections tothe main line) for faster shunting.

2.1. Preliminaries

Before the simulation model can be run, some key model parameters need to be calculatedgiven the scenarios/variables proposed by the millers, harvesters and growers. The first ofthese is the planned delivery times of rail bins to each harvester. For a planning horizonof T days, a sample of information known in advance for each harvester is contained inTable 1. Given that Harvester 1 starts at 4:00 am each day it operates, and cuts 66 tonnes ofcane per hour, it will fill 66/5.75 = 11.47 rail bins per hour. If only 50 bins can be stored atSiding 505 at any one time (either due to siding length limitations, pulling capacity of thelocomotive or a predefined maximum allotment size), then the harvester will need a set of50 bins at siding 505 every 50/11.47 = 4.3 h. Therefore, for Harvester 1 to not wait for bins,it will need an allotment of 50 bins at Siding 505 at 4:00 am, 50 bins at 8:18 am, 50 binsat 12:36 pm, and its last 19 bins at 4:54 pm. This planned delivery timetable is calculatedfor each harvester in the mill region. In light of this, letxij be the time when harvesteri ∈ Istarts filling itsj ∈ Jth allotment during the planning horizon,�xij be the planned time whenthere are no delays to the delivery of the allotment,sij be the number of rail bins in theallotment, andmi be the processing rate of harvesteri ∈ I in terms of bins per hour. Theone-way travel time from the mill to the siding where allotmentj ∈ J is to be delivered ishij hours. InTable 1, average bin weights do vary across harvesters due to differences inharvesting practices (Sandell and Agnew, 2002) and haulout equipment, as well as the sizeof the rail bins that the harvester uses. The harvester operator will usually plan in advance,and produce the list of sidings to be visited each day of the harvest season.

Table 1A sample of harvesting details that would be known in advance

Harvester Start time(am)

Tonnes/h Rail bin capacity(tonnes)

Tonnes/day Siding Day 1 Siding Day 2· · · Siding DayT

1 4.00 66 5.75 974 505 461 5174 0.30 56 5.80 1191 541 541 5396 6.00 59 5.64 470 420 414 4407 5.00 60 3.28 454 518 522 5188 5.00 64 3.81 647 470 477 4729 5.30 60 5.90 505 405 404 404


Fig. 2. Example of impact of location of harvester on rake size.

A key parameter to be estimated is average rake size. While a locomotive may be capableof pulling a rake of 200 bins, its average rake size during travel will be less than this due to:(1) dropping/pickup of bins to several sidings on a single run, (2) travelling considerabledistances between the drop off of bins to sidings, and (3) travelling to or from the millwith no bins. The average rake size being pulled by the locomotive is heavily impacted bythe location of the sidings being visited relative to one another. The more spread out theharvesters are geographically, the smaller the average rake size. This is illustrated inFig. 2using a two harvester, single branch line example. Here, both harvesters are to receive anallotment of 40 bins each in a delivery and the locomotive has a capacity of 100 bins. Thesidings are 5 km apart, with the first siding being 5 km from the mill. If the locomotivewere to deliver bins to both harvesters, it would travel to Harvester 1 with 80 bins, thencontinue to Harvester 2 with 40 bins. InFig. 2A, the harvesters are working close togetherwith Harvester 1 being 15 km from the mill and Harvester 2 being 20 km from the mill.Therefore the locomotive will pull 80 bins to the siding where Harvester 1 is located and thenwill travel another 5 km with 40 bins to where Harvester 2 is located. The average numberof bins being pulled by the locomotive on the journey is (15 km× 80 + 5 km× 40)/20 = 70bins. InFig. 2B, Harvesters 1 and 2 are delivering to sidings 5 and 30 km from the mill,respectively. While the average distance to the mill is the same as inFig. 2A, the locomotivewill travel 25 of the 30 km with only 40 bins, resulting in an average rake size of only 47bins for the trip. For the scenario listed inFig. 2B, 70/47 times as much locomotive capacitywill be required to service the harvesters compared toFig. 2A. Using the same principles toestimate the average rake size inFig. 2example, letab be the average rake size on branchline b∈B, ignoring the physical capacity of the locomotive. The only problem withab isthat it can be larger than the capacity of the locomotive,�a, when there are a large number ofharvesters supplying sidings on branch lineb∈B. Under such circumstances,ab is adjustedthrough multiplying it by the capacity of the locomotive divided by the sum of the allotmentsizes (sij ) of harvesters supplying to sidings on branch lineb∈B. The average rake size overthe network isa=

∑b ∈ B ab/(number of branch lines inB).

2.2. Simulation model

Given the planned times�xij that harvesteri ∈ I commences processing allotmentj ∈ J, aswell as the corresponding allotment sizessij , the simulation model estimates key events inservicing the harvesters as shown inFig. 3. Event 1 (Fig. 3) is initially calculated prior to thestart of the simulation byyij = �xij − hij, while the other events are calculated during the


Fig. 3. Sample timeline of events for an allotment of bins.

simulation. In Event 1, a locomotive will usually pull more than one allotment of rail binsduring the journey with an average rake size ofab bins. The transportation time to the sidingto which harvesteri ∈ I is delivering (highlighted in checkered pattern) is estimated as afunction of travel time to the siding plus shunt time plus the expected time to service otherharvesters delivering to sidings between harvesteri ∈ I and the mill. The allotment of railbins will sometimes be delivered to the siding prior to when the harvester is ready to startfilling them, particularly when it is the first allotment for the day for the harvester. Sincethe simulation model is based on unscheduled traffic, it cannot calculate the exact timesfor Events 1, 2 and 5 (Fig. 3). However, these times can be estimated accurately on mostoccasions. When the allotment of rail bins is not the first one of the day for the harvester,the allotment will need to arrive at the siding (Event 2) immediately after the harvester hasfilled the bins of its previous allotment. Thus, Events 2 and 3 will occur at the same time.For the first allotment of the day for the harvester, Events 1 and 2 can take place any timebetween the finishing time of the harvester on the previous day and the start time on thecurrent day. The simulation model can spread out Events 1 and 2 for the first allotment ofrail bins for each harvester to spread out the demand on locomotives. Unless the allotmentof rail bins is the last of the day for the harvester, the allotment will be transported tothe mill immediately after the harvester has finished filling the bins. This transport of fullbins will allow the next allotment for the harvester to be inserted into the siding withoutcreating waiting time for the harvester. When an allotment of rail bins is transported tothe mill (Event 5), it is not processed immediately since there is almost always a queue ofallotments waiting in the mill’s marshalling yard. Because the time window of harvesting isusually less than 24 h per day, whereas the mill processes cane continuously, the queue timeprior to processing can be several hours during the night. The simulation model assumes afirst come first served rule for the arrival of allotments into the mill marshalling yard. Thequeue time of allotmentj ∈ Jof harvesteri ∈ I isqij . The allotment of bins will become freeagain (Event 7) once they have been processed through the mill and new rakes of emptybins formed within the marshalling yard. The length of time for marshalling of full or emptybins ismt.

The simulation model will generate a set of events (Fig. 3) for every allotmentj ∈ Jfor each harvesteri ∈ I within the planning horizon. Within each time intervalt∈T of theplanning horizon, the number of rail bins in use,nt, is calculated by summing upsij (over


j ∈ J and i ∈ I) for which yij < t< rij . In the case of locomotives in use, the unproductivetime of a locomotive needs to be accounted for, since a locomotive will be out of actionfor servicing, meal breaks and crew changes. The proportion of a locomotive shift beingunproductive ranges between 15 and 30% in Australian sugar mills. An estimate of thenumber of locomotives in use at timet∈T= lt = ∑

i ∈ I

∑j ∈ Jyij<t<rij

sij/(a(1 − c)) where

c is the proportion of a locomotive shift that is unproductive.Every mill has a limited number of rail bins in its fleet,N, and locomotives,L, available,

for which lt ≤L andnt ≤N. Whenlt andnt reach their upper limits, delays will occur toyij and/orzij of lengthdij . If yij is delayed long enough, the allotment will arrive at timexij > �xij. In this case the harvester will be forced to wait, resulting in an increase in the totalharvest hours for the day. If there is not enough locomotive capacity for allotmentj ∈ J attimezij , the transportation of full rail bins to the mill will be delayed. This delay will haveno impact on the harvester, unless the harvester needs a further allotment at the siding onthe same day.

A basic version of the simulation model is described using Algorithm 1, which doesnot include yard locomotive activities or the spreading out of Events 1 and 2 for the firstallotment of the day.

Algorithm 1.

FOR each time intervalt ∈ T from 1 to the number of intervals in the planning horizonFOR each allotment of rail bins wherefij=t !Routine accounts for everything after

arrival at millCalculateqij based on the tonnes of cane queued for processing and mill processingratepij=fij + max(mt,qij )rij =pij +mtFOR each intervaltt∈T betweenfij andpij inclusive, letntt =ntt +sijFOR each intervaltt∈T betweenpij −mtandpij +mt inclusive, letltt = ltt + sij/

�a

ENDFORFOR each allotment of rail bins wherezij = t !Routine accounts for departure from

siding and arrival at millIF lt +sij /(a(1− c)) ≤L THENzij =zij + 1nt =nt +sij

ELSEfij =zij +hijFOR each intervaltt∈T betweenzij andfij − 1 inclusive, letltt = ltt +sij /(a(1− c))FOR each intervaltt∈T betweenzij andfij − 1 inclusive, letntt =ntt +sij

ENDIFENDFOR


FOR each allotment of rail bins whereyij = t !Departures of empties from mill, arrivalat siding and departure from sidingIF lt +sij /(a(1− c)) ≤L AND nt +sij ≤N THENxij = max(�xij,yij +hij ) !Actual arrival time of empty bins at the sidingFOR each intervaltt∈T betweenyij andyij +hij let ltt = ltt +sij /(a(1− c))zij =xij +sij /mi

FOR each intervaltt∈T betweenyij andzij − 1 letntt =ntt +sijELSEyij =yij + 1

ENDIFENDFOR

ENDFOR

The simulation model of Algorithm 1 divides the planning horizon into small timeintervals of fifteen minutes for the case study in this paper. At each of the time intervals(in chronological order), the simulation model checks whether any of the major events ofFig. 3need to be calculated for any of the allotments of the harvesters. In Algorithm 1, thecalculation of the events is divided into three sections. In the first section, the model detectsif any of the arrival times at the mill,fij , are within the current time intervalt∈T. If so,the model will then calculate the queue time followed by the processing time at the mill.The second major section calculates the arrival time of the allotment at the mill,fij , but isconditional upon whether there is enough locomotive capacity to transport the allotment tothe mill. If there is not enough capacity, the departure from the siding will not occur in thattime interval, and the simulation model will re-assess the availability of locomotive capacityin the next time interval. The third section detects if there are any planned departures,yij ,from the mill at time intervalt∈T. Subject to bin fleet and locomotive availability, the timewhen the harvester starts filling the allotment,xij , is calculated. If bin or locomotive capacityis not available, the departure of empty bins from the mill will not take place in intervalt∈T. When an event is calculated in each of the three sections of the simulation model, thenumber of bins in use,nt, and locomotive capacity used,lt, are updated.

The three sections of the simulation model are in reverse order of events to properlyaccount for delays due to shortage of bins and locomotive capacity. Once an allotmentof bins has departed from the mill, it is essential that its events are calculated (as wellas updating of the bin and locomotive usage as a result of the events), before calculatingearlier events of other allotments. This order of events will ensures that bin and locomotiveavailability will be known upon determining if an allotment of empty bins can depart fromthe mill in intervalt∈T.

3. Case study application

The Mourilyan sugar region produces an average of about 820,000 tonnes of cane peryear over an area of 12,160 ha and 243 farms. A diagram of the cane land and cane railnetwork (Fig. 4) shows that the track network stretches about 45 km from north to south andcontains 143 sidings in total. There are a large number of short branch lines, more than theaverage of 12–15 branch lines in a typical Australian sugar mill. The land under sugarcane


Fig. 4. Map of the Mourilyan sugar region showing the rail track network and cane land serviced by each harvester.

is highlighted by the different shades/colours, where each represents cane serviced by adifferent harvester. The region is serviced by 21 harvesters in total, though only 15 of themare of a significant size.

In Mourilyan, a steering group comprised of growing, harvesting and milling representa-tives was established and contained about 30 members in total to represent the local industry.The steering group collectively formulated a range of scenarios that they anticipated wouldpotentially deliver benefits to the region without being impossible to implement. For allscenarios, the Mourilyan steering group assumed a total cane yield of 850,000 tonnes and


Table 2Transportation impacts of scenarios formulated by Mourilyan steering group

Scenario Locomotive shiftsper day

Bin fleetneeded

Average cut-to-crushtime (h)

Time spent waiting for bins(% of total harvest hours)

Base case 15 1362 7.5 15.010 harvesters, 24 h har-

vesting window13 956 4.4 0.3

Harvest and transportwhole-of-crop to mill,with no trashblanketing

19 1696 7.2 10.9

a mill crush rate of 370 tonnes/h. For the base case, it was assumed that all 15 major har-vesters would have the same start times as experienced during the 2002 season. The capacityplanning model used a full harvest season planning horizon using the data from 2002. Thisapproach provided the added capability of assessing the day-to-day variability of impactson transport utilisation and the time that harvesters spend waiting for bins. The simulationmodel for capacity planning was coded in Lahey Fortran 95 on a PC with a Pentium M1.6 GHz processor. The CPU time was 15 s for one scenario using the full harvest seasonplanning horizon with 15 min time intervals.

Three very different scenarios are contained inTable 2, which highlight the wide rangeof scenarios/variables that the model can accommodate while measuring the impacts ofchange. On average, 15% of the total harvest hours were spent waiting for bins in the basecase, though harvester operators would use some of this time for servicing and meal breaks.The Mourilyan steering group considered 10 harvesters scheduled to operate at varioustimes over a 24 h period (seeFig. 5 for the hours of harvest as compared to the base case,where daylight hours harvesting is the primary schedule) as one of the scenarios for possibleimplementation in the future. The 10-harvester scenario produced significant improvementsin transportation, with the waiting time for bins being almost totally removed. Implementa-tion of a 10-harvester scenario would be difficult since harvesters are privately owned, andwould require incentives (or pay outs) for some harvester owners to leave the industry, orbusiness structures to financially encourage amalgamations to take place. Another potentialissue with only having 10 harvesters is reduced lesser ability to accommodate delays dueto breakdowns and wet weather. Having 10 harvesters scheduled over 24 h is close to ajust-in-time system, which means the mill is at a high risk at running out of cane (comparedto the base case), should a harvester break down or rain disrupt harvest.

A scenario was tested on whole-of-crop harvest, which means the entire cane plantis transported to the mill instead of just taking the stalks. While the leaf matter of thecane plant contains minimal sugar, the mill can process it to produce by-products such aselectricity or garden mulch. However, this extra extraneous matter (e.g., leaves and tops) ledto more plant material needing to be transported to the mill with the average weight in eachrail bin decreasing by approximately 30%. To accommodate whole-of-crop harvesting, anadditional 200 bins were assumed to be available, along with two additional locomotives.As a result, the capacity planning model showed that whole-of-crop harvest would requirean additional four locomotive shifts while maintaining an average waiting time for bins of

96A.H

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Fig. 5. Hours of harvest of each major harvester in base case of 2002 (left) and 10 harvesters (right).


Table 3Transportation impacts of modifying the harvest time window

Scenario Locomotiveshifts per day

Bin fleetneeded

Average cut-to-crushtime (h)

Time spent waiting for bins(% of total harvest hours)

Base case (12 h harvest window) 15 1362 7.5 15.015 h harvest window 15 1265 7.1 3.518 h harvest window 15 1241 6.4 1.521 h harvest window 15 1147 5.9 0.623 h harvest window 14 1128 5.1 0.524 h harvest window 14 1109 4.9 0.4

about 11% (4% less than for the base case). If only 150 additional bins were available, thewaiting time for bins would be higher than the base case and prone to high delays frombreakdowns and wet weather. When applying the other models ofFig. 1(e.g., Harvest Haulmodel), the scenarios ofTable 2can be fully costed for each sector of the sugar industry.

A scenario that the Mourilyan steering group considered for implementation in 2003 wasto keep the same harvesters but to stagger the start times so that harvesting is carried outover a longer time window than the daylight hours in the base case. The base case (Fig. 5)shows that if Harvesters 1 and 4 are removed, harvesting is carried out over a time window ofabout 12 h. In order to select a time window for implementation in 2003, the steering groupwished to see the impact of various time windows, for which longer time windows meantthere was harvesting during the night time. InTable 3, all parameters except locomotiveshifts decreased with increased harvesting time window, with the time that harvesters spentwaiting for bins being nearly eliminated. Average cut-to-crush time dropped from 7.5 h(base case) to about 5 h when there is harvesting over a 24 h time window.

Impacts of upgrading sidings were another scenario considered. Upgrading a sidingmeant converting it to a crossing loop, which allows the shunt time to be reduced by about50%. In selecting the sidings to be upgraded, sidings were sorted in descending order oftonnes of cane utilisation within any given year. Several scenarios were tested from 5–50sidings being upgraded and the impacts are shown inFig. 6. With increased number ofsidings upgraded, the largest savings were with the time spent waiting for bins, thoughthere are direct monetary savings to the mill with reduced locomotive shifts. Upgrades insidings had minimal effects on bin fleet requirements or cut-to-crush time. While calculatinga monetary benefit-to-cost ratio is out of the scope of this paper, this analysis could be usedwithin the modelling framework ofFig. 1to determine how many and which sidings shouldbe upgraded, accounting for the costs of upgrades. Assuming that funding is available forsiding upgrades, the implementation can be easily accomplished by the mill, while theharvester operators will also achieve benefits to their procedures.

For implementation in 2003, the main focus of the Mourilyan steering group was toincrease the time window of harvest to 18 h (i.e., the start times of harvesters are staggeredso that there is harvesting between 3:00 am and 9:00 pm during each day of harvest), whilemaintaining the existing number of harvesters. WhileTable 3shows that the benefits increasewith increased harvesting time window, a time window longer than 18 h (3:00 am to 9:00 pm)would receive too much resistance from the harvester contractors, even though they favouredreductions in waiting time for bin allotments. During the implementation in 2003, mill


Fig. 6. Impacts of upgrading sidings when sidings are sorted in descending order of utilisation.

representatives of the Mourilyan steering group agreed the estimates produced by the modelwere in close alignment with what was happening on the ground. This includes the impactsin peak periods of traffic where there are potential shortages of bins through to predictionof periods where the mill might run out of cane. An evaluation workshop was held with theMourilyan steering group in late 2003. The milling representatives agreed it was a bettersystem than in 2002. While the growing and harvesting representatives reported instancesof long waiting times for bins, these were attributed to early season wet weather and alearning curve for traffic planners to produce traffic schedules given the new harvester starttimes.

4. Extension to optimise harvester timetables

While the capacity planning simulation model can be independently applied to explorevarious scenarios, it can also be extended to optimise variables of the harvesting and transportsystem for further cost reductions. In this section, we use the capacity planning model tooptimise the start times of the harvesters, rather than setting them using expert knowledgeas was the case for the scenarios of the previous section. While this approach may appearto be prescriptive, given that the simulation model was designed to measure the impact ofscenarios, it does provide the milling, growing and harvesting representatives with a goal(in an ideal world), when negotiating alternative start times with harvester operators.

The objective in optimising start times of harvesters is to minimise costs of transportto the millers and delays to the harvesters, which the latter leads to a social inconvenienceand a “hard to measure” increased cost to harvesting. To capture this economic and socialobjective, we defineZ=λ1Z1 +λ2Z2 +λ3Z3 as a multi-objective function withz1 beingthe total number of locomotive shifts,z2 the total rail bin fleet requirements, andz3 thetotal waiting for bins that harvesters experience. Parametersλ1 to λ3 are the corresponding


weights withλ1Z1 +λ2Z2 +λ3Z3 = 1. Fixed and variables costs can also be captured in theobjective function since increased locomotive shifts represents a marginal cost, but increasedlocomotive shifts beyond the size of the existing locomotive fleet requires the purchase ofnew locomotives. The same constraints apply to the rail bin fleet. However, optimisingthe start times of harvesters will lead to locomotive shift savings, which is a marginal costsaving. It is difficult to capture the full system impact of delays to harvesters into an objectivefunction. The milling, harvesting and growing representatives of the Mourilyan case studydid agree that it is easy to validate the solutions produced by the models using the objectivedefined above for different values ofλ1 toλ3, until a solution is produced with an acceptablebalance of savings to the millers and harvesters.

A threshold accepting heuristic (Dueck and Scheuer, 1990) was used to optimise theharvester start times and is described in Algorithm 2. In Algorithm 2, we defineZbestas thebest value ofZ found so far andω as the threshold parameter. In the threshold acceptingheuristic, a new solution is accepted if it is no worse thanω from the best solutionZbest.The neighbourhood of the current solutionZ# is a change to the start time of one of theharvesters. Parameterφ# is the maximum number of iterations without improving the bestsolution, before the search is intensified by replacing the current solutionZ# with the bestsolution found so far. Since each iteration of Algorithm 1 takes several seconds of CPUtime, a CPU time of several hours is required for Algorithm 2. For the results of this section,the CPU upper time limit was set to 12 h.

Algorithm 2.

Perform Algorithm 1 using the initial values of start times of harvesters and letZbestbe thesolution SET initial value ofω

REPEATSETφ = 0,Z# =ZbestREPEAT

RANDOMLY select a modified start time of a harvester from the neighbourhood ofZ# and apply Algorithm 1 to obtainZIF Zbest−Z>ω SETZ# =ZIF Z# <ZbestSETZbest=Z#, φ = 0ADD 1 to φ

UNTIL φ =φ#

UPDATE the value ofωUNTIL CPU time > upper limit

When applying Algorithm 2, we initially setz1, z2 to 0.5 each to give equal priority tothese objectives. Since Mourilyan has an existing rail bin fleet, there was no real savingsby reducing the number of bins required, thusz2 = 0. Fig. 7shows the difference betweenthe 18 h time window scenario implemented each day of the harvest season during 2003using expert knowledge to set the start times versus optimal start times using Algorithm 2.The solution produced by Algorithm 2 led to a reduced waiting time for bins experiencedby harvesters by over 70% as compared to the 18 h time window scenario (Table 3). Forthe 2004 harvest season, the Mourilyan steering group will use the optimisation approach

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Fig. 7. Scenario of 18 h time window implemented in 2003 (left), and equivalent with optimised harvester start times (right).


to help improve the implementation of an 18 h harvesting time window, with harvestingconducted between 3:00 am and 9:00 pm. However, the steering group will still need tooverride some decisions of the model to account for the reluctance of some harvesters tocomply with the start times suggested by the model.

5. Conclusions and future developments

A simulation model for capacity planning was developed as part of a whole-of-systemmodelling framework to assess scenarios for cost reductions in harvesting and transport. Fora given scenario, the model measures the impacts of locomotive shifts, bin requirements, andthe time that harvesters spend waiting for bins. The benefits of the model were demonstratedthrough application to a case study mill region in Australia. It helped provide representativesof the case study region with an understanding of the system impacts from increasing the timewindow of harvesting as well as some big picture scenarios such as rationalised harvestinggroups and rail sidings. As a result of this understanding, the region moved away fromharvesting in daylight hours and implemented an 18 h harvesting time window during the2003 harvest season, for which harvester operators collectively staggered their start times sothat there was harvesting between the hours of 3:00 am and 9:00 pm. Such implementationachieved a more efficient transport service for the harvester operators using the existinglocomotive and rail bin fleet.

Through extending the simulation model for an optimisation capability and using thethreshold accepting heuristic, the start times of harvesters were optimised leading to furtherbenefits in harvesting and transport. Future developments will be to integrate the capac-ity planning model with other existing models for the harvesting and transport system tomeasure the system wide impacts of other important industry scenarios. These includeproducing schedules of harvester movements across sidings to minimise transport and har-vester movement costs, and optimising the start times of locomotive shifts to achieve thebest coverage of shifts versus demands for a transport service.

Acknowledgements

The authors would like to thank Mr. Richard Rees and Mr. Lindsay Wheeler of Bund-aberg Sugar for their commitment in formulating data, providing expert knowledge andmodel validation. Acknowledgement also goes to the financial support from the Australianfederal government and the sugar industry through the Sugar Research and DevelopmentCorporation.

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a simulation model for capacity planning in sugarcane transport

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