Research output: Working paper
Research output: Working paper
}
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 -