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Provably Near-Optimal LP-Based Policies for Revenue Management in Systems with Reusable Resources

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  • Retsef Levi

    (Sloan School of Management, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139)

  • Ana Radovanović

    (Google Inc., New York, New York 10011)

Abstract

Motivated by emerging applications in workforce management, we consider a class of revenue management problems in systems with reusable resources. The corresponding applications are modeled using the well-studied loss network systems . We use an extremely simple linear program (LP) that provides an upper bound on the best achievable expected long-run revenue rate. The optimal solution of the LP is used to devise a conceptually simple control policy that we call the class selection policy (CSP). Moreover, the LP is used to analyze the performance of the CSP and show that it admits uniform performance guarantees. In particular, for the model with a single resource and uniform resource requirements, we prove that the CSP is guaranteed to have an expected long-run revenue rate that is at least half of the best achievable. Furthermore, as the capacity of the system grows to infinity, the CSP is asymptotically optimal, regardless of any other parameter of the problem. Finally, our techniques can be used to analyze the performance of the well-known class of trunk-reservation policies.

Suggested Citation

  • Retsef Levi & Ana Radovanović, 2010. "Provably Near-Optimal LP-Based Policies for Revenue Management in Systems with Reusable Resources," Operations Research, INFORMS, vol. 58(2), pages 503-507, April.
    • Handle: RePEc:inm:oropre:v:58:y:2010:i:2:p:503-507
    DOI: 10.1287/opre.1090.0714
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    References listed on IDEAS

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    1. MILLER, Bruce L., 1969. "A queueing reward system with several customer classes," LIDAM Reprints CORE 41, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
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    3. Bruce L. Miller, 1969. "A Queueing Reward System with Several Customer Classes," Management Science, INFORMS, vol. 16(3), pages 234-245, November.
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    Cited by:

    1. Huseyin Topaloglu & S. Ilker Birbil & J. B. G. Frenk & Nilay Noyan, 2012. "Tractable Open Loop Policies for Joint Overbooking and Capacity Control Over a Single Flight Leg with Multiple Fare Classes," Transportation Science, INFORMS, vol. 46(4), pages 460-481, November.
    2. 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.
    3. Xiao-Yue Gong & Vineet Goyal & Garud N. Iyengar & David Simchi-Levi & Rajan Udwani & Shuangyu Wang, 2022. "Online Assortment Optimization with Reusable Resources," Management Science, INFORMS, vol. 68(7), pages 4772-4785, July.
    4. Doan, Xuan Vinh & Lei, Xiao & Shen, Siqian, 2020. "Pricing of reusable resources under ambiguous distributions of demand and service time with emerging applications," European Journal of Operational Research, Elsevier, vol. 282(1), pages 235-251.
    5. Stefanus Jasin & Amitabh Sinha, 2015. "An LP-Based Correlated Rounding Scheme for Multi-Item Ecommerce Order Fulfillment," Operations Research, INFORMS, vol. 63(6), pages 1336-1351, December.
    6. Huanan Zhang & Cong Shi & Chao Qin & Cheng Hua, 2016. "Stochastic regret minimization for revenue management problems with nonstationary demands," Naval Research Logistics (NRL), John Wiley & Sons, vol. 63(6), pages 433-448, September.
    7. Jacob Feldman & Nan Liu & Huseyin Topaloglu & Serhan Ziya, 2014. "Appointment Scheduling Under Patient Preference and No-Show Behavior," Operations Research, INFORMS, vol. 62(4), pages 794-811, August.
    8. Chamberlain, Jonathan & Simhon, Eran & Starobinski, David, 2021. "Preemptible queues with advance reservations: Strategic behavior and revenue management," European Journal of Operational Research, Elsevier, vol. 293(2), pages 561-578.
    9. Ward Whitt & Jingtong Zhao, 2017. "Many‐server loss models with non‐poisson time‐varying arrivals," Naval Research Logistics (NRL), John Wiley & Sons, vol. 64(3), pages 177-202, April.
    10. Santiago R. Balseiro & Jon Feldman & Vahab Mirrokni & S. Muthukrishnan, 2014. "Yield Optimization of Display Advertising with Ad Exchange," Management Science, INFORMS, vol. 60(12), pages 2886-2907, December.
    11. Paat Rusmevichientong & Mika Sumida & Huseyin Topaloglu, 2020. "Dynamic Assortment Optimization for Reusable Products with Random Usage Durations," Management Science, INFORMS, vol. 66(7), pages 2820-2844, July.

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