Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/75928
Title: SAA-regularized methods for multiproduct price optimization under the pure characteristics demand model
Authors: Sun, HL
Su, CL
Chen, XJ 
Keywords: Stochastic linear complementarity problem
Epi-convergence
Lower/upper semicontinuous
Sample average approximation
Regularized monotone linear complementarity problem
Multiproduct pricing
Issue Date: 2017
Publisher: Springer
Source: Mathematical programming, 2017, v. 165, no. 1, special issue SI, p. 361-389 How to cite?
Journal: Mathematical programming 
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.
URI: http://hdl.handle.net/10397/75928
ISSN: 0025-5610
EISSN: 1436-4646
DOI: 10.1007/s10107-017-1119-6
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