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Network Cargo Capacity Management

Author

Listed:
  • Tatsiana Levina

    (School of Business, Queen's University, Kingston, Ontario K7L 3N6, Canada)

  • Yuri Levin

    (School of Business, Queen's University, Kingston, Ontario K7L 3N6, Canada)

  • Jeff McGill

    (School of Business, Queen's University, Kingston, Ontario K7L 3N6, Canada)

  • Mikhail Nediak

    (School of Business, Queen's University, Kingston, Ontario K7L 3N6, Canada)

Abstract

We consider the problem faced by an airline that is flying both passengers and cargo over a network of locations on a fixed periodic schedule. Bookings for many classes of cargo shipments between origin-destination pairs in this network are made in advance, but the weight and volume of aircraft capacity available for cargo as well as the exact weight and volume of each shipment are not known at the time of booking. The problem is to control cargo accept/reject decisions to maximize expected profits while ensuring effective dispatch of accepted shipments through the network. This network stochastic dynamic control problem has very high computational complexity. We propose a linear programming and stochastic simulation-based computational method for learning approximate control policies and discuss their structural properties. The proposed method is flexible and can utilize historical booking data as well as decisions generated by default control policies.

Suggested Citation

  • Tatsiana Levina & Yuri Levin & Jeff McGill & Mikhail Nediak, 2011. "Network Cargo Capacity Management," Operations Research, INFORMS, vol. 59(4), pages 1008-1023, August.
    • Handle: RePEc:inm:oropre:v:59:y:2011:i:4:p:1008-1023
    DOI: 10.1287/opre.1110.0929
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    References listed on IDEAS

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    2. Moussawi-Haidar, Lama & Nasr, Walid & Jalloul, Maya, 2021. "Standardized cargo network revenue management with dual channels under stochastic and time-dependent demand," European Journal of Operational Research, Elsevier, vol. 295(1), pages 275-291.
    3. Eric Larsen & Sébastien Lachapelle & Yoshua Bengio & Emma Frejinger & Simon Lacoste-Julien & Andrea Lodi, 2022. "Predicting Tactical Solutions to Operational Planning Problems Under Imperfect Information," INFORMS Journal on Computing, INFORMS, vol. 34(1), pages 227-242, January.
    4. Shaban, I.A. & Wang, Z.X. & Chan, F.T.S. & Chung, S.H. & Eltoukhy, A.E.E. & Qu, T., 2019. "Price setting for extra-baggage service for a combination carrier using the newsvendor setup," Journal of Air Transport Management, Elsevier, vol. 78(C), pages 1-14.
    5. He, Wen, 2019. "Impact of capacity flexibility on the use of booking limits," European Journal of Operational Research, Elsevier, vol. 274(1), pages 199-213.
    6. Klein, Robert & Koch, Sebastian & Steinhardt, Claudius & Strauss, Arne K., 2020. "A review of revenue management: Recent generalizations and advances in industry applications," European Journal of Operational Research, Elsevier, vol. 284(2), pages 397-412.
    7. Michael F. Gorman & John-Paul Clarke & Amir Hossein Gharehgozli & Michael Hewitt & René de Koster & Debjit Roy, 2014. "State of the Practice: A Review of the Application of OR/MS in Freight Transportation," Interfaces, INFORMS, vol. 44(6), pages 535-554, December.
    8. Lawrence C. Leung & Gang Chen & Yer Van Hui & Wen He, 2016. "An Airfreight Forwarder’s Shipment Bidding and Logistics Planning," Transportation Science, INFORMS, vol. 50(1), pages 275-287, February.
    9. Wu, You & Lange, Anne & Mantin, Benny, 2022. "Who benefits from air service agreements? The case of international air cargo operations," Transportation Research Part B: Methodological, Elsevier, vol. 163(C), pages 281-303.
    10. Justin Dumouchelle & Emma Frejinger & Andrea Lodi, 2024. "Reinforcement learning for freight booking control problems," Journal of Revenue and Pricing Management, Palgrave Macmillan, vol. 23(4), pages 318-345, August.

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