Network revenue management with inventory-sensitive bid prices and customer choice
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.
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"An Enhanced Concave Program Relaxation for Choice Network Revenue Management,"
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- 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.
- 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.
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