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An Execution Planning Algorithm for Military Airlift

Author

Listed:
  • Rajendra S. Solanki

    (Oak Ridge National Laboratory, PO Box 2008, Oak Ridge, Tennessee 37831-6366)

  • Frank Southworth

    (Oak Ridge National Laboratory, PO Box 2008, Oak Ridge, Tennessee 37831-6206)

Abstract

Execution planning at the military airlift command of the US Air Force involves updating the routes and schedules of aircraft to reflect changing requirements for moving freight and personnel and changing airlift resources. The problem can be characterized as a pickup and delivery problem with time windows and node capacities. The constraints on the capacities of the nodes (airfields) limit the rate at which the aircraft can be serviced and the amount of requirements (tonnage of cargo, number of passengers) that can be loaded and unloaded at an airfield per day. The execution planning algorithm described can be used daily to modify the existing airlift operations plan. An algorithm based on the insertion heuristic was selected for implementation due to its computational feasibility and its capability to absorb complex constraints arising in the execution planning problem. The insertion heuristic has the additional desirable feature of keeping intact as much of the existing schedule as possible.

Suggested Citation

  • Rajendra S. Solanki & Frank Southworth, 1991. "An Execution Planning Algorithm for Military Airlift," Interfaces, INFORMS, vol. 21(4), pages 121-131, August.
    • Handle: RePEc:inm:orinte:v:21:y:1991:i:4:p:121-131
    DOI: 10.1287/inte.21.4.121
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    Citations

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    Cited by:

    1. Barbarosoglu, Gulay & Ozdamar, Linet & Cevik, Ahmet, 2002. "An interactive approach for hierarchical analysis of helicopter logistics in disaster relief operations," European Journal of Operational Research, Elsevier, vol. 140(1), pages 118-133, July.
    2. C Guéret & N Jussien & O Lhomme & C Pavageau & C Prins, 2003. "Loading aircraft for military operations," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 54(5), pages 458-465, May.
    3. Mahmoudi, Monirehalsadat & Zhou, Xuesong, 2016. "Finding optimal solutions for vehicle routing problem with pickup and delivery services with time windows: A dynamic programming approach based on state–space–time network representations," Transportation Research Part B: Methodological, Elsevier, vol. 89(C), pages 19-42.
    4. Soumia Ichoua & Michel Gendreau & Jean-Yves Potvin, 2000. "Diversion Issues in Real-Time Vehicle Dispatching," Transportation Science, INFORMS, vol. 34(4), pages 426-438, November.

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