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An Approximate Dynamic Programming Approach to Network Revenue Management with Customer Choice

Author

Listed:
  • Dan Zhang

    (Desautels Faculty of Management, McGill University, Montreal, Quebec H3A 1G5, Canada)

  • Daniel Adelman

    (Graduate School of Business, University of Chicago, Chicago, Illinois 60637)

Abstract

We consider a network revenue management problem where customers choose among open fare products according to some prespecified choice model. Starting with a Markov decision process (MDP) formulation, we approximate the value function with an affine function of the state vector. We show that the resulting problem provides a tighter bound for the MDP value than the choice-based linear program . We develop a column generation algorithm to solve the problem for a multinomial logit choice model with disjoint consideration sets (MNLD). We also derive a bound as a by-product of a decomposition heuristic. Our numerical study shows the policies from our solution approach can significantly outperform heuristics from the choice-based linear program.

Suggested Citation

  • Dan Zhang & Daniel Adelman, 2009. "An Approximate Dynamic Programming Approach to Network Revenue Management with Customer Choice," Transportation Science, INFORMS, vol. 43(3), pages 381-394, August.
    • Handle: RePEc:inm:ortrsc:v:43:y:2009:i:3:p:381-394
    DOI: 10.1287/trsc.1090.0262
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    References listed on IDEAS

    as
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