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

Dan Zhang () and Daniel Adelman ()
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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

Transportation Science, 2009, vol. 43, issue 3, 381-394

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.

Keywords: network revenue management; choice behavior; dynamic programming (search for similar items in EconPapers)
Date: 2009
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Citations: View citations in EconPapers (89)

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Persistent link: https://EconPapers.repec.org/RePEc:inm:ortrsc:v:43:y:2009:i:3:p:381-394

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