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Performance of an LP-Based Control for Revenue Management with Unknown Demand Parameters

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  • Stefanus Jasin

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

Abstract

We consider a standard network revenue management (RM) problem and study the performance of a linear program (LP)-based control, the Probabilistic Allocation Control (PAC), in the presence of unknown demand parameters. We show that frequent re-optimizations of PAC without re-estimation suffice to shrink the asymptotic impact of estimation error on revenue loss. If, in addition to re-optimizations, we also frequently re-estimate the parameters, we prove that the performance of PAC in the unknown parameters setting is almost as good as the performance of PAC in the known parameters setting. Our numerical experiments show that PAC yields a revenue improvement of order 0.5%–1.5% relative to LP-based Booking Limit and Bid Price in most cases. Given the small margin in RM industries, such as the airline industry (about 2%), this level of improvement can easily translate into a significant increase in profit.

Suggested Citation

  • Stefanus Jasin, 2015. "Performance of an LP-Based Control for Revenue Management with Unknown Demand Parameters," Operations Research, INFORMS, vol. 63(4), pages 909-915, August.
    • Handle: RePEc:inm:oropre:v:63:y:2015:i:4:p:909-915
    DOI: 10.1287/opre.2015.1390
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    References listed on IDEAS

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    1. Guillermo Gallego & Garrett van Ryzin, 1997. "A Multiproduct Dynamic Pricing Problem and Its Applications to Network Yield Management," Operations Research, INFORMS, vol. 45(1), pages 24-41, February.
    2. Martin I. Reiman & Qiong Wang, 2008. "An Asymptotically Optimal Policy for a Quantity-Based Network Revenue Management Problem," Mathematics of Operations Research, INFORMS, vol. 33(2), pages 257-282, May.
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    4. Stefanus Jasin & Sunil Kumar, 2013. "Analysis of Deterministic LP-Based Booking Limit and Bid Price Controls for Revenue Management," Operations Research, INFORMS, vol. 61(6), pages 1312-1320, December.
    5. Dan Zhang & Daniel Adelman, 2009. "An Approximate Dynamic Programming Approach to Network Revenue Management with Customer Choice," Transportation Science, INFORMS, vol. 43(3), pages 381-394, August.
    6. Stefanus Jasin & Sunil Kumar, 2012. "A Re-Solving Heuristic with Bounded Revenue Loss for Network Revenue Management with Customer Choice," Mathematics of Operations Research, INFORMS, vol. 37(2), pages 313-345, May.
    7. Dragos Florin Ciocan & Vivek Farias, 2012. "Model Predictive Control for Dynamic Resource Allocation," Mathematics of Operations Research, INFORMS, vol. 37(3), pages 501-525, August.
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    Cited by:

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    2. Catherine Cleophas & Daniel Kadatz & Sebastian Vock, 2017. "Resilient revenue management: a literature survey of recent theoretical advances," Journal of Revenue and Pricing Management, Palgrave Macmillan, vol. 16(5), pages 483-498, October.
    3. Negin Gorlezaei & Patrick Jaillet & Zijie Zhou, 2022. "Online Resource Allocation with Samples," Papers 2210.04774, arXiv.org.
    4. Pornpawee Bumpensanti & He Wang, 2020. "A Re-Solving Heuristic with Uniformly Bounded Loss for Network Revenue Management," Management Science, INFORMS, vol. 66(7), pages 2993-3009, July.
    5. Qi (George) Chen & Stefanus Jasin & Izak Duenyas, 2019. "Nonparametric Self-Adjusting Control for Joint Learning and Optimization of Multiproduct Pricing with Finite Resource Capacity," Mathematics of Operations Research, INFORMS, vol. 44(2), pages 601-631, May.
    6. Dawsen Hwang & Patrick Jaillet & Vahideh Manshadi, 2021. "Online Resource Allocation Under Partially Predictable Demand," Operations Research, INFORMS, vol. 69(3), pages 895-915, May.
    7. Ezgi C. Eren & Zhaoyang Zhang & Jonas Rauch & Ravi Kumar & Royce Kallesen, 2024. "Revenue management without demand forecasting: a data-driven approach for bid price generation," Journal of Revenue and Pricing Management, Palgrave Macmillan, vol. 23(6), pages 499-516, December.

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