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Cargo Capacity Management with Allotments and Spot Market Demand

Author

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  • Yuri Levin

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

  • Mikhail Nediak

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

  • Huseyin Topaloglu

    (School of Operations Research and Information Engineering, Cornell University, Ithaca, New York 14853)

Abstract

We consider a problem faced by an airline that operates a number of parallel flights to transport cargo between a particular origin to destination pair. The airline can sell its cargo capacity either through allotment contracts or on the spot market, where customers exhibit choice behavior between different flights. The goal is to simultaneously select allotment contracts among available bids and find a booking control policy for the spot market to maximize the sum of the profit from the allotments and the total expected profit from the spot market. We formulate the booking control problem on the spot market as a dynamic program and construct approximations to its value functions, which can be used to estimate the total expected profit from the spot market. We show that our value function approximations provide upper bounds on the optimal total expected profit from the spot market, and they allow us to solve the allotment selection problem through a sequence of linear mixed-integer programs with a special structure. Furthermore, the value function approximations are useful for constructing a booking control policy for the spot market with desirable monotonic properties. Computational experiments show that the proposed approach can be scaled to realistic problems and provides well-performing allotment allocation and booking control decisions.

Suggested Citation

  • Yuri Levin & Mikhail Nediak & Huseyin Topaloglu, 2012. "Cargo Capacity Management with Allotments and Spot Market Demand," Operations Research, INFORMS, vol. 60(2), pages 351-365, April.
    • Handle: RePEc:inm:oropre:v:60:y:2012:i:2:p:351-365
    DOI: 10.1287/opre.1110.1023
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    References listed on IDEAS

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    Cited by:

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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.
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    5. Delgado, Felipe & Trincado, Ricardo & Pagnoncelli, Bernardo K., 2019. "A multistage stochastic programming model for the network air cargo allocation under capacity uncertainty," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 131(C), pages 292-307.
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    11. Arim Park & Min Kyung Lee & Hyosoo (Kevin) Park & Ji-Hung (Ryan) Choi & Ju Myung Song, 2023. "The impact of security oversight on air cargo price and demand," Journal of Transportation Security, Springer, vol. 16(1), pages 1-22, December.
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    13. Ghilas, Veaceslav & Demir, Emrah & Woensel, Tom Van, 2016. "A scenario-based planning for the pickup and delivery problem with time windows, scheduled lines and stochastic demands," Transportation Research Part B: Methodological, Elsevier, vol. 91(C), pages 34-51.
    14. Bo Feng & Zheyu Jiang & Fujun Lai, 2020. "Robust approach for air cargo freight forwarder selection under disruption," Annals of Operations Research, Springer, vol. 291(1), pages 339-360, August.
    15. Lin, Danping & Lee, Carman Ka Man & Yang, Jilin, 2017. "Air cargo revenue management under buy-back policy," Journal of Air Transport Management, Elsevier, vol. 61(C), pages 53-63.
    16. Delgado, Felipe & Sirhan, Cristóbal & Katscher, Mathias & Larrain, Homero, 2020. "Recovering from demand disruptions on an air cargo network," Journal of Air Transport Management, Elsevier, vol. 85(C).
    17. 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.
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    19. 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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