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Network Revenue Management with Inventory-Sensitive Bid Prices and Customer Choice

Research output: Working paper

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Network Revenue Management with Inventory-Sensitive Bid Prices and Customer Choice. / Meissner, J; Strauss, A K.

Lancaster University : The Department of Management Science, 2010. (Management Science Working Paper Series).

Research output: Working paper

Harvard

Meissner, J & Strauss, AK 2010 'Network Revenue Management with Inventory-Sensitive Bid Prices and Customer Choice' Management Science Working Paper Series, The Department of Management Science, Lancaster University.

APA

Meissner, J., & Strauss, A. K. (2010). Network Revenue Management with Inventory-Sensitive Bid Prices and Customer Choice. (Management Science Working Paper Series). The Department of Management Science.

Vancouver

Meissner J, Strauss AK. Network Revenue Management with Inventory-Sensitive Bid Prices and Customer Choice. Lancaster University: The Department of Management Science. 2010. (Management Science Working Paper Series).

Author

Meissner, J ; Strauss, A K. / Network Revenue Management with Inventory-Sensitive Bid Prices and Customer Choice. Lancaster University : The Department of Management Science, 2010. (Management Science Working Paper Series).

Bibtex

@techreport{6fceba4cace3469d9fe155ea58a78a46,
title = "Network Revenue Management with Inventory-Sensitive Bid Prices and Customer Choice",
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, 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.",
keywords = "revenue management, bid prices, dynamic programming/optimal control: applications, approximate dynamic programming.",
author = "J Meissner and Strauss, {A K}",
year = "2010",
language = "English",
series = "Management Science Working Paper Series",
publisher = "The Department of Management Science",
type = "WorkingPaper",
institution = "The Department of Management Science",

}

RIS

TY - UNPB

T1 - Network Revenue Management with Inventory-Sensitive Bid Prices and Customer Choice

AU - Meissner, J

AU - Strauss, A K

PY - 2010

Y1 - 2010

N2 - 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.

AB - 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.

KW - revenue management

KW - bid prices

KW - dynamic programming/optimal control: applications

KW - approximate dynamic programming.

M3 - Working paper

T3 - Management Science Working Paper Series

BT - Network Revenue Management with Inventory-Sensitive Bid Prices and Customer Choice

PB - The Department of Management Science

CY - Lancaster University

ER -