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Dynamic booking control for car rental revenue management: A decomposition approach

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  • Li, Dong
  • Pang, Zhan

Abstract

This paper considers dynamic booking control for a single-station car rental revenue management problem. Different from conventional airline revenue management, car rental revenue management needs to take into account not only the existing bookings but also the lengths of the existing rentals and the capacity flexibility via fleet shuttling, which yields a high-dimensional system state space. In this paper, we formulate the dynamic booking control problem as a discrete-time stochastic dynamic program over an infinite horizon. Such a model is computationally intractable. We propose a decomposition approach and develop two heuristics. The first heuristic is an approximate dynamic program (ADP) which approximates the value function using the value functions of the decomposed problems. The second heuristic is constructed directly from the optimal booking limits computed from the decomposed problems, which is more scalable compared to the ADP heuristic. Our numerical study suggests that the performances of both heuristics are close to optimum and significantly outperform the commonly used probabilistic non-linear programming (PNLP) heuristic in most of the instances. The dominant performance of our second heuristic is evidenced in a case study using sample data from a major car rental company in the UK.

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  • Li, Dong & Pang, Zhan, 2017. "Dynamic booking control for car rental revenue management: A decomposition approach," European Journal of Operational Research, Elsevier, vol. 256(3), pages 850-867.
    • Handle: RePEc:eee:ejores:v:256:y:2017:i:3:p:850-867
    DOI: 10.1016/j.ejor.2016.06.044
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    Cited by:

    1. Nikolaos Kourentzes & Dong Li & Arne K. Strauss, 2019. "Unconstraining methods for revenue management systems under small demand," Journal of Revenue and Pricing Management, Palgrave Macmillan, vol. 18(1), pages 27-41, February.
    2. Dong Li & Zhan Pang & Lixian Qian, 2023. "Bid price controls for car rental network revenue management," Production and Operations Management, Production and Operations Management Society, vol. 32(1), pages 261-282, January.
    3. Julien Guillen & Pierre Ruiz & Umberto Dellepiane & Ludovica Maccarrone & Raffaele Maccioni & Alessandro Pinzuti & Enrico Procacci, 2019. "Europcar Integrates Forecasting, Simulation, and Optimization Techniques in a Capacity and Revenue Management System," Interfaces, INFORMS, vol. 49(1), pages 1-40, January.
    4. Naragain Phumchusri & Phatsakorn Sangsukiam & Nannapat Chariyasethapong, 2020. "Optimal overbooking model for car rental business with two levels of prices having stochastic joint booking and show-up levels," Journal of Revenue and Pricing Management, Palgrave Macmillan, vol. 19(3), pages 190-209, June.
    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. Bayliss, Christopher & Currie, Christine S.M. & Bennell, Julia A. & Martinez-Sykora, Antonio, 2019. "Dynamic pricing for vehicle ferries: Using packing and simulation to optimize revenues," European Journal of Operational Research, Elsevier, vol. 273(1), pages 288-304.
    7. 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.
    8. Yu, Yugang & Dong, Yuxuan & Guo, Xiaolong, 2018. "Pricing for sales and per-use rental services with vertical differentiation," European Journal of Operational Research, Elsevier, vol. 270(2), pages 586-598.
    9. Otero, Daniel F. & Escallón, Mariana & López, Cristina & Akhavan-Tabatabaei, Raha, 2019. "Optimal timing of airline promotions under dilution," European Journal of Operational Research, Elsevier, vol. 277(3), pages 981-995.

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