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Coffee, Tea, or …?: A Markov Decision Process Model for Airline Meal Provisioning

Author

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  • Jason H. Goto

    (Centre for Operations Excellence, University of British Columbia, 209-2053 Main Mall, Vancouver, British Columbia, Canada V6T 1Z2)

  • Mark E. Lewis

    (Department of Industrial and Operations Engineering, University of Michigan, 1205 Beal Avenue, Ann Arbor, Michigan 48109-2117)

  • Martin L. Puterman

    (Sauder School of Business, University of British Columbia, 2053 Main Mall, Vancouver, British Columbia, Canada V6T 1Z2)

Abstract

This paper develops and analyzes a finite horizon Markov decision process model for the airline meal provisioning activity focusing explicitly on developing policies for determining and revising the number of meals to upload. Using one year of daily data from over 40 flights, the paper shows that the optimal policies can result in both improved customer service and significant dollar savings, especially in long haul flights. It also applies the model to derive an efficient frontier and investigate tradeoffs between having too few and too many meals on a flight.

Suggested Citation

  • Jason H. Goto & Mark E. Lewis & Martin L. Puterman, 2004. "Coffee, Tea, or …?: A Markov Decision Process Model for Airline Meal Provisioning," Transportation Science, INFORMS, vol. 38(1), pages 107-118, February.
    • Handle: RePEc:inm:ortrsc:v:38:y:2004:i:1:p:107-118
    DOI: 10.1287/trsc.1030.0022
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    References listed on IDEAS

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    Citations

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    Cited by:

    1. Yiting Xing & Ling Li & Zhuming Bi & Marzena Wilamowska‐Korsak & Li Zhang, 2013. "Operations Research (OR) in Service Industries: A Comprehensive Review," Systems Research and Behavioral Science, Wiley Blackwell, vol. 30(3), pages 300-353, May.
    2. Wu, Xin & Nie, Lei & Xu, Meng & Yan, Fei, 2018. "A perishable food supply chain problem considering demand uncertainty and time deadline constraints: Modeling and application to a high-speed railway catering service," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 111(C), pages 186-209.
    3. Wei, Ying & Xiong, Sijia & Li, Feng, 2019. "Ordering bias with two reference profits: Exogenous benchmark and minimum requirement," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 128(C), pages 229-250.
    4. Li, Sijie & Zhu, Zhanbei & Huang, Lihua, 2009. "Supply chain coordination and decision making under consignment contract with revenue sharing," International Journal of Production Economics, Elsevier, vol. 120(1), pages 88-99, July.
    5. Birisci, Esma & McGarvey, Ronald G., 2016. "Inferring shortfall costs and integrating environmental costs into optimal production levels for an all-you-care-to-eat food service operation," International Journal of Production Economics, Elsevier, vol. 182(C), pages 157-164.
    6. Wang, Charles X. & Webster, Scott, 2009. "The loss-averse newsvendor problem," Omega, Elsevier, vol. 37(1), pages 93-105, February.
    7. Wu, Xin & Nie, Lei & Xu, Meng & Zhao, Lili, 2019. "Distribution planning problem for a high-speed rail catering service considering time-varying demands and pedestrian congestion: A lot-sizing-based model and decomposition algorithm," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 123(C), pages 61-89.
    8. Eunmin (Min) Hwang & Yen-Soon Kim & Hyo Geun Song, 2023. "Airline Passengers’ Willingness to Reserve Inflight Meals Online and Their Willingness to Pay for Meal Upgrades: The Case Study of U.S. Students," Sustainability, MDPI, vol. 15(10), pages 1-22, May.
    9. Song, Yang & Fan, Tijun & Tang, Yuewu & Xu, Chang, 2021. "Omni-channel strategies for fresh produce with extra losses in-store," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 148(C).

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