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A revenue management model for products with two capacity dimensions

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  • Xiao, Baichun
  • Yang, Wei

Abstract

Many perishable products and services have multiple capacity attributes. Shipping capacity of container liners, for example, is measured by both volume and weight. Containers with different size consume various capacities in the two dimensions. Restaurant revenue management aims to maximize the revenue per available seat-hour that captures both the number of dining tables and service manpower. Similar issues arise in the air cargo, trucking and health care industries. We study the revenue management problem with two capacity features and formulate the problem as a continuous-time stochastic control model. Unlike heuristic approaches, we derive the optimal solution in an analytical form. Computation of the optimal solution is fairly efficient. With certain conditions we explore the structural properties of the optimal solution. We show that if the revenue rate is concave in the capacity usage, the expected value of marginal capacity is monotone. As a result, the control policy is featured by a sequence of thresholds which displays a significant difference when the remaining capacity-mix varies. Numerical examples are provided.

Suggested Citation

  • Xiao, Baichun & Yang, Wei, 2010. "A revenue management model for products with two capacity dimensions," European Journal of Operational Research, Elsevier, vol. 205(2), pages 412-421, September.
    • Handle: RePEc:eee:ejores:v:205:y:2010:i:2:p:412-421
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    Cited by:

    1. Moussawi-Haidar, Lama & Nasr, Walid & Jalloul, Maya, 2021. "Standardized cargo network revenue management with dual channels under stochastic and time-dependent demand," European Journal of Operational Research, Elsevier, vol. 295(1), pages 275-291.
    2. Tatsiana Levina & Yuri Levin & Jeff McGill & Mikhail Nediak, 2011. "Network Cargo Capacity Management," Operations Research, INFORMS, vol. 59(4), pages 1008-1023, August.
    3. Yuri Levin & Mikhail Nediak & Huseyin Topaloglu, 2012. "Cargo Capacity Management with Allotments and Spot Market Demand," Operations Research, INFORMS, vol. 60(2), pages 351-365, April.
    4. Long Gao & Jim (Junmin) Shi & Michael F. Gorman & Ting Luo, 2020. "Business Analytics for Intermodal Capacity Management," Manufacturing & Service Operations Management, INFORMS, vol. 22(2), pages 310-329, March.
    5. Andreea Popescu & Earl Barnes & Ellis Johnson & Pinar Keskinocak, 2013. "Bid Prices When Demand Is a Mix of Individual and Batch Bookings," Transportation Science, INFORMS, vol. 47(2), pages 198-213, May.
    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. Michael F. Gorman & John-Paul Clarke & Amir Hossein Gharehgozli & Michael Hewitt & René de Koster & Debjit Roy, 2014. "State of the Practice: A Review of the Application of OR/MS in Freight Transportation," Interfaces, INFORMS, vol. 44(6), pages 535-554, December.
    9. Lin, Danping & Lee, Carman Ka Man & Yang, Jilin, 2017. "Air cargo revenue management under buy-back policy," Journal of Air Transport Management, Elsevier, vol. 61(C), pages 53-63.
    10. Moussawi-Haidar, Lama, 2014. "Optimal solution for a cargo revenue management problem with allotment and spot arrivals," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 72(C), pages 173-191.

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