1. Studies in Route Optimization of Cargo Airlines in India Dr. Rajkumar S. Pant Associate Professor of Aerospace Engineering Indian Institute of Technology, Bombay rkpant@aero.iitb.ac.in

2. Airports Routes Aircraft Scheduled Flights A B C D Typical Airline Network Airports Aircraft Routes Schedule Time varying Demand

3. Literature Review Objectives – Kanafani (1982),Teodorovic (1988) Max. Revenue Min. Cost Max. Profit Max. Level of Service Max. Aircraft Utilization

4. Literature Review Objectives – Kanafani (1982),Teodorovic (1988) Multi Criteria Model – Teodorovic & Krcmar-nozic (1989) Max. Profit Max. number of passenger flown Min. Schedule Delay

5. 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

6. 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)

7. 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

8. 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

9. 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

10. 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

11. 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

12. 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

13. Methodology for Airline Network Scheduling and Optimization

14. 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

15. Inputs required Airport Details Network Details Demand Data Base Station Details Fleet Details Route Priorities (if any)

16. Overview of the methodology Control Parameters Demand index Cost Index Time Index Route Priority Index

17. Schedule Generator

18. Objective Functions Max. CargoTotal Cargo carried over all the routes Min. CostTotal Operating Cost over all the routes Min. TimeTotal flight time of all aircraft on all routes Min. QOS Variance Difference between required and allotted frequency on all OD pairs Max. Cargo/CostRatio of total amount of Cargo carried over the network with the Total Operating Cost incurred Max. Cargo/timeRatio of total amount of Cargo carried over the network and summation of the total flight time of all aircraft on all routes

19. Constraints Airport Slots Break Even Load Factor Base Station and Hanger Capacity Maintenance

20. Case Study for Overnight Express Cargo Airline

21. Overnight Express Cargo Late night cutoffs, early morning delivery Varying demand Dedicated Freighter aircraft Fixed window for Flight Operations

22. Assumptions Dedicated Cargo airline Demand is known a priori Route Lengths Harmonic Range Same Turn Around Time at all airports

23. 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

24. Typical Results Improvements compared to existing schedule being operated

25. Sample Output Objective function CargoCostTimeQOS VarianceCargo/TimeCargo/Cost Max Cargo1.2181.1171.4222.3390.8561.090 Min Cost0.9240.8851.1672.1340.7921.043 Max Time1.0201.1381.4902.9970.6850.897 Min QOS Variance1.2311.0341.3390.8980.9201.191 Max Cargo/Cost Max Cargo/Time1.2781.0161.2970.9780.9851.258

26. Conclusions Methodology for demand responsive scheduling of days operation Grey Theory for route selection Genetic Algorithms for Optimization Case Study for Express Cargo airline ~ 20% improvement Cargo Carried Cargo/Cost

27. Thank you

28. By Deng (1982) ParametersDefinitionsExamples in Airline Network Candidates (C 1,C 2,C 3.. ) List of Possible solutionsDirect flight Indirect flights Properties/ Index (P 1,P 2,P 3.. ) Figure of merits on which the selection is based Number of Intermediate Stops m rsc Route Length Index Traffic concentration Categories (Cat 1,Cat 2, Cat 3 … ) List of possible decisions to which a candidate can belong Select Reject Probable Whitening Functions Instrument to take decisionLess than a number Greater than a number Approximate to a number - Can handle systems for which exact information is lacking - Can deal with multidisciplinary characteristics of the system Grey Theory

29. Whitening Functions Greater then a numberLess then a numberApprox to a number 3 Types