Network revenue management with inventory-sensitive bid prices and customer choice
► Solution method for network revenue management problems with improved accuracy compared to other methods. ► Approximate dynamic programming approach with an arbitrary aggregation of inventory units. ► The algorithm allows a trade-off between solution quality and runtime. We develop an approximate...
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| Vydáno v: | European journal of operational research Ročník 216; číslo 2; s. 459 - 468 |
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| Hlavní autoři: | , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
Amsterdam
Elsevier B.V
16.01.2012
Elsevier Elsevier Sequoia S.A |
| Témata: | |
| ISSN: | 0377-2217, 1872-6860 |
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| Abstract | ► Solution method for network revenue management problems with improved accuracy compared to other methods. ► Approximate dynamic programming approach with an arbitrary aggregation of inventory units. ► The algorithm allows a trade-off between solution quality and runtime.
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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| AbstractList | 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. [PUBLICATION ABSTRACT] 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. ► Solution method for network revenue management problems with improved accuracy compared to other methods. ► Approximate dynamic programming approach with an arbitrary aggregation of inventory units. ► The algorithm allows a trade-off between solution quality and runtime. 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. |
| Author | Meissner, Joern Strauss, Arne |
| Author_xml | – sequence: 1 givenname: Joern surname: Meissner fullname: Meissner, Joern email: joe@meiss.com organization: Kühne Logistics University, Hamburg, Germany – sequence: 2 givenname: Arne surname: Strauss fullname: Strauss, Arne email: a.strauss@lancaster.ac.uk organization: Lancaster University Management School, Lancaster, United Kingdom |
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| Keywords | Approximate Revenue management Dynamic programming/optimal control: applications Dynamic programming/optimal control Markov decision Income Non linear function Return rate Network management Real time Modeling Workload Aggregation Logistic regression Upper bound Optimal control Inventory control Logit model Dynamic programming Tariffication applications |
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| SubjectTerms | Agglomeration Applied sciences Approximate Approximation Decision theory. Utility theory Dynamic programming Dynamic programming/optimal control: applications Exact sciences and technology Inventories Inventory control, production control. Distribution Inventory management Markov analysis Markov processes Mathematical analysis Mathematical functions Mathematical models Mathematical programming Mathematics Networks Operational research and scientific management Operational research. Management science Probability and statistics Probability theory and stochastic processes Revenue management Revenues Sciences and techniques of general use Stockpiling Studies |
| Title | Network revenue management with inventory-sensitive bid prices and customer choice |
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