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Real-Time Dynamic Pricing for Revenue Management with Reusable Resources, Advance Reservation, and Deterministic Service Time Requirements

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  • Yanzhe (Murray) Lei

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

  • Stefanus Jasin

    (Stephen M. Ross School of Business, University of Michigan, Ann Arbor, Michigan 48109)

Abstract

We consider a dynamic pricing problem in a system with reusable resources. Customers arrive randomly over time according to a specified nonstationary rate, and each customer requests a service that uses a combination of different types of resources for a deterministic duration of time. The resources are reusable in the sense that they can be immediately used to serve a new customer on the completion of the previous service. Our objective is to construct a dynamic pricing control that maximizes expected total revenues. This is a fundamental problem faced by firms in many industries. We develop real-time heuristic controls based on the solution of the deterministic relaxation of the original stochastic problem and show that they are near optimal in the regime of large demand and large resource capacity. We further show that our results can be extended to a more general setting with heterogeneous service time and advance reservation.

Suggested Citation

  • Yanzhe (Murray) Lei & Stefanus Jasin, 2020. "Real-Time Dynamic Pricing for Revenue Management with Reusable Resources, Advance Reservation, and Deterministic Service Time Requirements," Operations Research, INFORMS, vol. 68(3), pages 676-685, May.
    • Handle: RePEc:inm:oropre:v:68:y:2020:i:3:p:676-685
    DOI: 10.1287/opre.2019.1906
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    References listed on IDEAS

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    1. Yanzhe (Murray) Lei & Stefanus Jasin & Amitabh Sinha, 2018. "Joint Dynamic Pricing and Order Fulfillment for E-commerce Retailers," Manufacturing & Service Operations Management, INFORMS, vol. 20(2), pages 269-284, May.
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    Cited by:

    1. Nikhil Garg & Hamid Nazerzadeh, 2022. "Driver Surge Pricing," Management Science, INFORMS, vol. 68(5), pages 3219-3235, May.
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    3. Huijie Peng & Yan Cheng & Xingyuan Li, 2023. "Real-Time Pricing Method for Spot Cloud Services with Non-Stationary Excess Capacity," Sustainability, MDPI, vol. 15(4), pages 1-21, February.
    4. Yuan, Guanxiu & Gao, Yan & Ye, Bei, 2021. "Optimal dispatching strategy and real-time pricing for multi-regional integrated energy systems based on demand response," Renewable Energy, Elsevier, vol. 179(C), pages 1424-1446.

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