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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Bibliographic Details
Published in:European journal of operational research Vol. 216; no. 2; pp. 459 - 468
Main Authors: Meissner, Joern, Strauss, Arne
Format: Journal Article
Language:English
Published: Amsterdam Elsevier B.V 16.01.2012
Elsevier
Elsevier Sequoia S.A
Subjects:
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
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
ISSN:0377-2217, 1872-6860
Online Access:Get full text
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Description
Summary:► 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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ISSN:0377-2217
1872-6860
DOI:10.1016/j.ejor.2011.06.033