Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/75928
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Title: SAA-regularized methods for multiproduct price optimization under the pure characteristics demand model
Authors: Sun, H
Su, CL
Chen, X 
Issue Date: Sep-2017
Source: Mathematical programming, Sept. 2017, v. 165, no. 1, p. 361-389
Abstract: Utility-based choice models are often used to determine a consumer's purchase decision among a list of available products; to provide an estimate of product demands; and, when data on purchase decisions or market shares are available, to infer consumers' preferences over observed product characteristics. These models also serve as a building block in modeling firms' pricing and assortment optimization problems. We consider a firm's multiproduct pricing problem, in which product demands are determined by a pure characteristics model. A sample average approximation (SAA) method is used to approximate the expected market share of products and the firm profit. We propose an SAA-regularized method for the multiproduct price optimization problem. We present convergence analysis and numerical examples to show the efficiency and the effectiveness of the proposed method.
Keywords: Stochastic linear complementarity problem
Epi-convergence
Lower/upper semicontinuous
Sample average approximation
Regularized monotone linear complementarity problem
Multiproduct pricing
Publisher: Springer
Journal: Mathematical programming 
ISSN: 0025-5610
EISSN: 1436-4646
DOI: 10.1007/s10107-017-1119-6
Rights: © Springer-Verlag Berlin Heidelberg and Mathematical Optimization Society 2017
This version of the article has been accepted for publication, after peer review (when applicable) and is subject to Springer Nature’s AM terms of use (https://www.springernature.com/gp/open-research/policies/accepted-manuscript-terms), but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at: http://dx.doi.org/10.1007/s10107-017-1119-6
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