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Bid Prices When Demand Is a Mix of Individual and Batch Bookings

Author

Listed:
  • Andreea Popescu

    (Turner Broadcasting System, Inc., Atlanta, Georgia 30318)

  • Earl Barnes

    (H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332)

  • Ellis Johnson

    (H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332)

  • Pinar Keskinocak

    (H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332)

Abstract

Industries such as aviation, hospitality, and package tours often face both individual and batch bookings, requiring one unit and multiple units of capacity, respectively. Using bid prices is a common practice in accepting or rejecting an incoming booking (or equivalently, deciding which price bucket to offer to the incoming bookings at a given time). Most of the literature and existing applications for making accept/reject decisions model the arrival stream as individual bookings. In this paper, we propose an effective approach to determine bid prices when there is a mixed demand pattern with individual and batch bookings. We propose decomposing the demand into “small” and “large” bookings, using dynamic programming for large bookings, and a fast, high-quality approximation for small bookings. We present an application of our approach in the air cargo industry, where cargo is often separated into two categories, namely, mail and packages (small cargo) and freight (large cargo). To obtain bid prices for small cargo, we approximate cargo booking requests with passenger arrival models and develop an efficient and effective algorithm to solve the probabilistic nonlinear formulation of the seat allocation problem from the passenger literature. To obtain bid prices for large cargo, we solve a dynamic program decomposed by flight leg, which is tractable due to the scattered arrivals and large sizes of the bookings. Our approach leads to a significant potential increase in revenues compared to the first-come, first-served (FCFS) approach and the solution from the deterministic formulation (DIP), two methods commonly used in practice.

Suggested Citation

  • 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.
  • Handle: RePEc:inm:ortrsc:v:47:y:2013:i:2:p:198-213
    DOI: 10.1287/trsc.1120.0420
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    References listed on IDEAS

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    1. H Jiang, 2008. "A Lagrangian relaxation approach for network inventory control of stochastic revenue management with perishable commodities," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 59(3), pages 372-380, March.
    2. Daniel Adelman, 2007. "Dynamic Bid Prices in Revenue Management," Operations Research, INFORMS, vol. 55(4), pages 647-661, August.
    3. Kevin Pak & Nanda Piersma, 2002. "overview of OR techniques for airline revenue management," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 56(4), pages 479-495, November.
    4. A. Ciancimino & G. Inzerillo & S. Lucidi & L. Palagi, 1999. "A Mathematical Programming Approach for the Solution of the Railway Yield Management Problem," Transportation Science, INFORMS, vol. 33(2), pages 168-181, May.
    5. 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.
    6. Pak, K. & Dekker, R., 2004. "Cargo Revenue Management: Bid-Prices for a 0-1 Multi Knapsack Problem," ERIM Report Series Research in Management ERS-2004-055-LIS, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus University Rotterdam.
    7. Pak, K. & Piersma, N., 2002. "airline revenue management," ERIM Report Series Research in Management ERS-2002-12-LIS, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus University Rotterdam.
    8. Pak, K. & Piersma, N., 2002. "Airline revenue management: an overview of OR techniques 1982-2001," Econometric Institute Research Papers EI 2002-03, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    9. Nicola Secomandi, 2008. "An Analysis of the Control-Algorithm Re-solving Issue in Inventory and Revenue Management," Manufacturing & Service Operations Management, INFORMS, vol. 10(3), pages 468-483, December.
    10. Andreea Popescu & Pinar Keskinocak & Ellis Johnson & Mariana LaDue & Raja Kasilingam, 2006. "Estimating Air-Cargo Overbooking Based on a Discrete Show-Up-Rate Distribution," Interfaces, INFORMS, vol. 36(3), pages 248-258, June.
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    Cited by:

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    3. 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.

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