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Network Revenue Management with Inventory-Sensitive Bid Prices and Customer Choice

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Author Info

  • Joern Meissner

    (Department of Management Science, Lancaster University Management School)

  • Arne Strauss

    (Department of Management Science, Lancaster University Management School)

Abstract

We develop a new approximate dynamic programming approach to network revenue management models with customer choice that approximates the value function of the Markov decision process with a concave function which is separable across resource inventory levels. This approach reflects the intuitive interpretation of diminishing marginal utility of inventory levels and allows for significantly improved accuracy compared to currently available methods. The model 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, and is asymptotically optimal under fluid scaling. Computational experiments for the multinomial logit choice model with distinct consideration sets show that policies derived from our approach outperform available alternatives, and we demonstrate how aggregation can be used to balance solution quality and runtime.

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Bibliographic Info

Paper provided by Department of Management Science, Lancaster University in its series Working Papers with number MRG/0008.

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Length: 33 pages
Date of creation: Jul 2008
Date of revision: Apr 2010
Handle: RePEc:lms:mansci:mrg-0008

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Postal: LANCASTER LA1 4YX
Web page: http://www.lums.lancs.ac.uk/departments/ManSci/
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Keywords: revenue management; dynamic programming; optimal control; applications; approximate;

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References

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  1. Joern Meissner & Arne Strauss & Kalyan Talluri, 2011. "An enhanced concave program relaxation for choice network revenue management," Economics Working Papers 1259, Department of Economics and Business, Universitat Pompeu Fabra, revised Aug 2011.
  2. 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.
  3. 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.
  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. 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.
  6. 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.
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Citations

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Cited by:
  1. 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.
  2. 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.
  3. 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.
  4. 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.
  5. 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.
  6. 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.
  7. 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.
  8. 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.
  9. Jochen Goensch & Claudius Steinhardt, 2013. "Using Dynamic Programming Decomposition for Revenue Management with Opaque Products," BuR - Business Research, German Academic Association for Business Research, vol. 6(1), pages 94-115, May.
  10. Arne Strauss & Kalyan Talluri, 2012. "A Tractable Consideration Set Structure for Network Revenue Management," Working Papers 606, Barcelona Graduate School of Economics.
  11. Summit Kunnumkal & Kalyan Talluri, 2012. "A New Compact Linear Programming Formulation for Choice Network Revenue Management," Working Papers 677, Barcelona Graduate School of Economics.
  12. 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.
  13. 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.

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