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Network revenue management with inventory-sensitive bid prices and customer choice

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  • Meissner, Joern
  • Strauss, Arne

Abstract

We develop an approximate dynamic programming approach to network revenue management models with customer choice that approximates the value function of the Markov decision process with a non-linear function which is separable across resource inventory levels. This approximation can exhibit significantly improved accuracy compared to currently available methods. It further allows for arbitrary aggregation of inventory units and thereby reduction of computational workload, yields upper bounds on the optimal expected revenue that are provably at least as tight as those obtained from previous approaches. Computational experiments for the multinomial logit choice model with distinct consideration sets show that policies derived from our approach can outperform some recently proposed alternatives, and we demonstrate how aggregation can be used to balance solution quality and runtime.

Suggested Citation

  • Meissner, Joern & Strauss, Arne, 2012. "Network revenue management with inventory-sensitive bid prices and customer choice," European Journal of Operational Research, Elsevier, vol. 216(2), pages 459-468.
  • Handle: RePEc:eee:ejores:v:216:y:2012:i:2:p:459-468
    DOI: 10.1016/j.ejor.2011.06.033
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    References listed on IDEAS

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    1. Kalyan Talluri & Garrett van Ryzin, 2004. "Revenue Management Under a General Discrete Choice Model of Consumer Behavior," Management Science, INFORMS, vol. 50(1), pages 15-33, January.
    2. Joern Meissner & Arne Strauss & Kalyan Talluri, 2011. "An Enhanced Concave Program Relaxation for Choice Network Revenue Management," Working Papers 534, Barcelona Graduate School of Economics.
    3. Joern Meissner & Arne Strauss, 2009. "Choice-Based Network Revenue Management under Weak Market Segmentation," Working Papers MRG/0012, Department of Management Science, Lancaster University, revised May 2010.
    4. Garrett van Ryzin & Gustavo Vulcano, 2008. "Computing Virtual Nesting Controls for Network Revenue Management Under Customer Choice Behavior," Manufacturing & Service Operations Management, INFORMS, vol. 10(3), pages 448-467, October.
    5. Zhang, Dan & Cooper, William L., 2009. "Pricing substitutable flights in airline revenue management," European Journal of Operational Research, Elsevier, vol. 197(3), pages 848-861, September.
    6. Chen, Lijian & Homem-de-Mello, Tito, 2010. "Mathematical programming models for revenue management under customer choice," European Journal of Operational Research, Elsevier, vol. 203(2), pages 294-305, June.
    7. Wen-Chyuan Chiang & Jason C.H. Chen & Xiaojing Xu, 2007. "An overview of research on revenue management: current issues and future research," International Journal of Revenue Management, Inderscience Enterprises Ltd, vol. 1(1), pages 97-128.
    Full references (including those not matched with items on IDEAS)

    Citations

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    Cited by:

    1. Sumit Kunnumkal & Kalyan Talluri, 2014. "On the Tractability of the Piecewiselinear Approximation for General Discrete-Choice Network Revenue Management," Working Papers 749, Barcelona Graduate School of Economics.
    2. Sumit Kunnumkal & Kalyan Talluri, 2011. "Equivalence of Piecewise-Linear Approximation and Lagrangian Relaxation for Network Revenue Management," Working Papers 608, Barcelona Graduate School of Economics.
    3. David Sayah, 2015. "Approximate Linear Programming in Network Revenue Management with Multiple Modes," Working Papers 1518, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz.
    4. Nicolas Houy & François Le Grand, 2015. "The Monte Carlo first-come-first-served heuristic for network revenue management," Working Papers halshs-01155698, HAL.
    5. Joern Meissner & Arne Strauss & Kalyan Talluri, 2011. "An Enhanced Concave Program Relaxation for Choice Network Revenue Management," Working Papers MRG/0020, Department of Management Science, Lancaster University, revised Jan 2011.
    6. Steinhardt, Claudius & Gönsch, Jochen, 2012. "Integrated revenue management approaches for capacity control with planned upgrades," European Journal of Operational Research, Elsevier, vol. 223(2), pages 380-391.
    7. Huang, Kuancheng & Lin, Chia-Yi, 2014. "A simulation analysis for the re-solving issue of the network revenue management problem," Journal of Air Transport Management, Elsevier, vol. 38(C), pages 36-42.
    8. repec:eee:proeco:v:193:y:2017:i:c:p:352-364 is not listed on IDEAS
    9. Sumit Kunnumkal & Kalyan Talluri, 2011. "Equivalence of piecewise-linear approximation and Lagrangian relaxation for network revenue management," Economics Working Papers 1305, Department of Economics and Business, Universitat Pompeu Fabra, revised Nov 2012.
    10. repec:eee:ejores:v:265:y:2018:i:2:p:621-630 is not listed on IDEAS
    11. Meissner, Joern & Strauss, Arne, 2012. "Improved bid prices for choice-based network revenue management," European Journal of Operational Research, Elsevier, vol. 217(2), pages 417-427.
    12. Juan M. Chaneton & Gustavo Vulcano, 2011. "Computing Bid Prices for Revenue Management Under Customer Choice Behavior," Manufacturing & Service Operations Management, INFORMS, vol. 13(4), pages 452-470, October.
    13. Arne Strauss & Kalyan Talluri, 2012. "A Tractable Consideration Set Structure for Network Revenue Management," Working Papers 606, Barcelona Graduate School of Economics.
    14. Nicolas Houy & François Le Grand, 2015. "Financing and advising with (over)confident entrepreneurs : an experimental investigation," Working Papers 1514, Groupe d'Analyse et de Théorie Economique Lyon St-Étienne (GATE Lyon St-Étienne), Université de Lyon.
    15. Sumit Kunnumkal & Kalyan Talluri, 2012. "A New Compact Linear Programming Formulation for Choice Network Revenue Management," Working Papers 677, Barcelona Graduate School of Economics.
    16. Ødegaard, Fredrik & Wilson, John G., 2016. "Dynamic pricing of primary products and ancillary services," European Journal of Operational Research, Elsevier, vol. 251(2), pages 586-599.
    17. Li, Dong & Pang, Zhan, 2017. "Dynamic booking control for car rental revenue management: A decomposition approach," European Journal of Operational Research, Elsevier, vol. 256(3), pages 850-867.
    18. Sierag, D.D. & Koole, G.M. & van der Mei, R.D. & van der Rest, J.I. & Zwart, B., 2015. "Revenue management under customer choice behaviour with cancellations and overbooking," European Journal of Operational Research, Elsevier, vol. 246(1), pages 170-185.
    19. Hosseinalifam, M. & Marcotte, P. & Savard, G., 2016. "A new bid price approach to dynamic resource allocation in network revenue management," European Journal of Operational Research, Elsevier, vol. 255(1), pages 142-150.
    20. Arne Strauss & Kalyan Talluri, 2012. "A tractable consideration set structure for network revenue management," Economics Working Papers 1303, Department of Economics and Business, Universitat Pompeu Fabra, revised Oct 2012.
    21. Sumit Kunnumkal & Kalyan Talluri, 2012. "A new compact linear programming formulation for choice network revenue management," Economics Working Papers 1349, Department of Economics and Business, Universitat Pompeu Fabra.
    22. Mohammad Vardi & Ali Salmasnia & Ali Ghorbanian & Hadi Mokhtari, 2016. "A bi-objective airline revenue management problem with possible cancellation," International Journal of Applied Management Science, Inderscience Enterprises Ltd, vol. 8(1), pages 20-37.
    23. Sumit Kunnumkal & Kalyan Talluri, 2014. "On the tractability of the piecewise-linear approximation for general discrete-choice network revenue management," Economics Working Papers 1409, Department of Economics and Business, Universitat Pompeu Fabra.
    24. repec:eee:ejores:v:263:y:2017:i:3:p:935-945 is not listed on IDEAS

    More about this item

    Keywords

    Revenue management; Dynamic programming/optimal control: applications; Approximate;

    JEL classification:

    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis

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