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Joern Meissner, Arne K Strauss
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| Abstract |
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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, 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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| Keywords |
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Revenue Management, Dynamic Programming, Optimal Control, Applications, Approximate
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| Status |
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Working Paper |
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| Download |
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www.meiss.com/download/RM-Meissner-Strauss.pdf
(335 kb) |
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| Reference |
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BibTeX,
Plain Text |
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