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Using Prices More Realistically as Decision Variables in Perishable-Asset Revenue Management Problems

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  • Lawrence R. Weatherford

    (University of Wyoming)

Abstract

This paper addresses a new slant to a problem which is general to many of the transportation industries—perishable asset revenue management. Traditional approaches have assumed that prices are fixed and solved for the optimal allocation quantities. Our approach recognizes that prices of the different classes affect demand and should therefore be included as decision variables to be optimized. We solve three different types of problems: (a) up to n price classes, distinct asset control mechanism, and no diversion, (b) up to 3 price classes, serial nested asset control mechanism, and no diversion, (c) up to 3 price classes, serial nested asset control mechanism, and diversion. Analytical results are provided in most cases and examples illustrate the results as well as the time required to solve these complex problems. Finally we look at the tradeoff involved between computational time and expected contribution when using heuristic decisions obtained from less realistic assumptions relative to the true optimal decisions. On average, the suboptimality ranged from 3.19% to 4.88% with a corresponding decrease in computing time required on the order of several minutes. Some trends are presented to help determine a priori which type of problems would tend to benefit most from the more accurate formulation. This should help managers decide when it is worth the extra computing time to come up with the true optimal solution.

Suggested Citation

  • Lawrence R. Weatherford, 1997. "Using Prices More Realistically as Decision Variables in Perishable-Asset Revenue Management Problems," Journal of Combinatorial Optimization, Springer, vol. 1(3), pages 277-304, October.
    • Handle: RePEc:spr:jcomop:v:1:y:1997:i:3:d:10.1023_a:1009728426728
    DOI: 10.1023/A:1009728426728
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    References listed on IDEAS

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    Cited by:

    1. Dai, Yue & Chao, Xiuli & Fang, Shu-Cherng & Nuttle, Henry L.W., 2005. "Pricing in revenue management for multiple firms competing for customers," International Journal of Production Economics, Elsevier, vol. 98(1), pages 1-16, October.
    2. Jin Qin & Yijia Zeng & Xia Yang & Yuxin He & Xuanke Wu & Wenxuan Qu, 2019. "Time-Dependent Pricing for High-Speed Railway in China Based on Revenue Management," Sustainability, MDPI, vol. 11(16), pages 1-18, August.
    3. Becher, Michael, 2009. "Simultaneous capacity and price control based on fuzzy controllers," International Journal of Production Economics, Elsevier, vol. 121(2), pages 365-382, October.
    4. Hetrakul, Pratt & Cirillo, Cinzia, 2014. "A latent class choice based model system for railway optimal pricing and seat allocation," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 61(C), pages 68-83.
    5. Brian So, 2017. "An implementable optimization of airfare structure," Journal of Revenue and Pricing Management, Palgrave Macmillan, vol. 16(5), pages 441-465, October.
    6. Miju Ahn & Xiaodong Luo & Sergey Shebalov, 2020. "Variable pricing: an integrated airline pricing and revenue management model," Journal of Revenue and Pricing Management, Palgrave Macmillan, vol. 19(6), pages 421-435, December.
    7. Xu, Guangming & Zhong, Linhuan & Hu, Xinlei & Liu, Wei, 2022. "Optimal pricing and seat allocation schemes in passenger railway systems," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 157(C).
    8. Feng, Youyi & Xiao, Baichun, 2006. "Integration of pricing and capacity allocation for perishable products," European Journal of Operational Research, Elsevier, vol. 168(1), pages 17-34, January.
    9. Wai Hung Wong & Anming Zhang & Yer Van Hui & Lawrence C. Leung, 2009. "Optimal Baggage-Limit Policy: Airline Passenger and Cargo Allocation," Transportation Science, INFORMS, vol. 43(3), pages 355-369, August.
    10. Yu Wang & Xinghua Shan & Hongye Wang & Junfeng Zhang & Xiaoyan Lv & Jinfei Wu, 2022. "Ticket Allocation Optimization of Fuxing Train Based on Overcrowding Control: An Empirical Study from China," Sustainability, MDPI, vol. 14(12), pages 1-12, June.
    11. Oliveira, Beatriz Brito & Carravilla, Maria Antónia & Oliveira, José Fernando, 2017. "Fleet and revenue management in car rental companies: A literature review and an integrated conceptual framework," Omega, Elsevier, vol. 71(C), pages 11-26.
    12. Zhang, Michael & Bell, Peter C. & Cai, Gangshu (George) & Chen, Xiangfeng, 2010. "Optimal fences and joint price and inventory decisions in distinct markets with demand leakage," European Journal of Operational Research, Elsevier, vol. 204(3), pages 589-596, August.
    13. Ayşe Kocabıyıkoğlu & Ioana Popescu & Catalina Stefanescu, 2014. "Pricing and Revenue Management: The Value of Coordination," Management Science, INFORMS, vol. 60(3), pages 730-752, March.
    14. Achim I. Czerny & Erik T. Verhoef & Anming Zhang, 2015. "A Theory of Continuous Uncertainty Types," Tinbergen Institute Discussion Papers 15-065/VIII, Tinbergen Institute.
    15. William L. Cooper & Diwakar Gupta, 2006. "Stochastic Comparisons in Airline Revenue Management," Manufacturing & Service Operations Management, INFORMS, vol. 8(3), pages 221-234, February.
    16. Richard Ratliff & Ben Vinod, 2016. "An applied process for airline strategic fare optimization," Journal of Revenue and Pricing Management, Palgrave Macmillan, vol. 15(5), pages 320-333, October.

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