Studies in Route Optimization of Cargo Airlines in India
Dr. Rajkumar S. PantAssociate Professor of Aerospace Engineering
Indian Institute of Technology, [email protected]
Airports
Routes
Aircraft
Scheduled Flights
A
B
CD
Typical Airline Network
Airports
Aircraft
Routes
Schedule
Time varying Demand
Literature Review Objectives – Kanafani (1982),Teodorovic (1988)
Max. RevenueMin. CostMax. ProfitMax. Level of ServiceMax. Aircraft Utilization
Literature Review
Objectives – Kanafani (1982),Teodorovic (1988) Multi Criteria Model – Teodorovic & Krcmar-nozic (1989)
Max. ProfitMax. number of passenger flownMin. Schedule Delay
Literature Review
Objectives – Kanafani (1982),Teodorovic (1988) Multi Criteria Model – Teodorovic & Krcmar-nozic (1989) Day of Operation – Teodorovic and Stojkovic (1995)
Min. Canceled flights and Min. Total Passenger Delay
Literature Review
Objectives – Kanafani (1982),Teodorovic (1988) Multi Criteria Model – Teodorovic & Krcmar-nozic (1989) Day of Operation – Teodorovic and Stojkovic (1995) Fleet Assignment –Gvozdenovic (1999)
Literature Review
Objectives – Kanafani (1982),Teodorovic (1988) Multi Criteria Model – Teodorovic & Krcmar-nozic (1989) Day of Operation – Teodorovic and Stojkovic (1995) Fleet Assignment –Gvozdenovic (1999) Route Selection – Hsu and Wen (2000)
Application of Grey Theory
Literature Review
Objectives – Kanafani (1982),Teodorovic (1988) Multi Criteria Model – Teodorovic & Krcmar-nozic (1989) Day of Operation – Teodorovic and Stojkovic (1995) Fleet Assignment –Gvozdenovic (1999) Route Selection – Hsu and Wen (2000) Crew –Kornilakis et al (2002)
Crew pairing & Assignment
Literature Review
Objectives – Kanafani (1982),Teodorovic (1988) Multi Criteria Model – Teodorovic & Krcmar-nozic (1989) Day of Operation – Teodorovic and Stojkovic (1995) Fleet Assignment –Gvozdenovic (1999) Route Selection – Hsu and Wen (2000) Crew –Kornilakis et al (2002) Maintenance- Sriram and Haghani (2003)
Minimum Maintenance Cost
Literature Review
Objectives – Kanafani (1982),Teodorovic (1988) Multi Criteria Model – Teodorovic & Krcmar-nozic (1989) Day of Operation – Teodorovic and Stojkovic (1995) Fleet Assignment –Gvozdenovic (1999) Route Selection – Hsu and Wen (2000) Crew –Kornilakis et al (2002) Maintenance- Sriram and Haghani (2003) Departure Time: Chang & Schonfeld (2004), Pollack (1974)
Min. average schedule delay per passenger
Literature Review
Objectives – Kanafani (1982),Teodorovic (1988) Multi Criteria Model – Teodorovic & Krcmar-nozic (1989) Day of Operation – Teodorovic and Stojkovic (1995) Fleet Assignment –Gvozdenovic (1999) Route Selection – Hsu and Wen (2000) Crew –Kornilakis et al (2002) Maintenance- Sriram and Haghani (2003) Departure Time: Chang & Schonfeld (2004), Pollack (1974) Air Cargo fleet routing: Yan, Chen & Chen (2006)
Dedicated methodology for Cargo Airlines
Literature Review
Objectives – Kanafani (1982),Teodorovic (1988) Multi Criteria Model – Teodorovic & Krcmar-nozic (1989) Day of Operation – Teodorovic and Stojkovic (1995) Fleet Assignment –Gvozdenovic (1999) Route Selection – Hsu and Wen (2000) Crew –Kornilakis et al (2002) Maintenance- Sriram and Haghani (2003) Departure Time: Chang & Schonfeld (2004), Pollack (1974) Air Cargo fleet routing: Yan, Chen & Chen (2006) Integrated Transportation Network Design & Optimization- Taylor &
De-Weck (2007)
Optimization of Aircraft & Route Network at one go
Methodology for Airline Network Scheduling and Optimization
Features
Demand responsive, flexible scheduling Arrive at ‘‘Schedule-of-the-day“
Maintenance and operational constraints applicable
Combined scheduling and optimisation Route selection using Grey Theory (Deng, 1982)
Optimization of user-selectable objective functions
Airline can assign priorities to certain routes
Inputs required
Airport Details
Network Details
Demand Data
Base Station Details
Fleet Details
Route Priorities (if any)
Overview of the methodology
Control Parameters Demand index Cost Index Time Index Route Priority Index
Schedule Generator
Objective FunctionsMax. Cargo Total Cargo carried over all the routes
Min. Cost Total Operating Cost over all the routes
Min. Time Total flight time of all aircraft on all routes
Min. QOS Variance
Difference between required and allotted frequency on all OD pairs
Max. Cargo/Cost
Ratio of total amount of Cargo carried over the network with the Total Operating Cost incurred
Max. Cargo/time
Ratio of total amount of Cargo carried over the network and summation of the total flight time of all aircraft on all routes
Constraints
Airport Slots
Break Even Load Factor
Base Station and Hanger Capacity
Maintenance
Case Study for Overnight Express Cargo Airline
Overnight Express Cargo
Late night cutoffs, early morning
delivery
Varying demand
Dedicated Freighter aircraft
Fixed window for Flight Operations
Assumptions
Dedicated Cargo airline
Demand is known a priori
Route Lengths ≤ Harmonic Range
Same Turn Around Time at all airports
Constraints in Schedule Generation
Operational Airport Slot availability
Break-even Load Factor
Operating time window
Maintenance Base station to go to at the end of the day
Hangar Capacity
Maximum flight time available for each aircraft
Typical Results
18%
-12%
33%
8%
20%
-20%
-10%
0%
10%
20%
30%
40%
Cargo Cost Time Quality ofService
Cargo/Cost
Improvements compared to existing schedule being operated
Sample Output
Objective function Cargo Cost Time QOS Variance Cargo/Time Cargo/Cost
Max Cargo 1.218 1.117 1.422 2.339 0.856 1.090
Min Cost 0.924 0.885 1.167 2.134 0.792 1.043
Max Time 1.020 1.138 1.490 2.997 0.685 0.897
Min QOS Variance 1.231 1.034 1.339 0.898 0.920 1.191
Max Cargo/CostMax Cargo/Time 1.278 1.016 1.297 0.978 0.985 1.258
Conclusions
Methodology for demand responsive scheduling of day’s operation Grey Theory for route selection Genetic Algorithms for Optimization
Case Study for Express Cargo airline ~ 20% improvement
Cargo Carried Cargo/Cost
Thank you
By Deng (1982)
Parameters Definitions Examples in Airline Network
Candidates
(C1,C2,C3..)
List of Possible solutions Direct flight
Indirect flights
Properties/
Index
(P1,P2,P3..)
Figure of merits on which the selection is based
Number of Intermediate Stops mrsc
Route Length Index
Traffic concentration
Categories
(Cat1,Cat2, Cat3… )
List of possible decisions to which a candidate can belong
Select
Reject
Probable
Whitening Functions
Instrument to take decision Less than a number
Greater than a number
Approximate to a number
rsc
rsc
- Can handle systems for which exact information is lacking
- Can deal with multidisciplinary characteristics of the system
Grey Theory Grey Theory
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Whitening Functions
Greater then a numberLess then a number Approx to a number
3 Types