SAA-regularized methods for multiproduct price optimization under the pure characteristics demand model

• 成果类型: Article
• 署名作者: Sun, Hailin; Su, Che-Lin; Chen, Xiaojun
• 署名单位: Nanjing University of Science & Technology; University of Chicago; Hong Kong Polytechnic University
• 刊物名称: MATHEMATICAL PROGRAMMING
• ISSN/ISSBN: 0025-5610
• DOI: 10.1007/s10107-017-1119-6
• 发表日期: 2017
• 页码: 361-389
• 关键词: Nested Logit Model; Assortment Optimization; algorithm

摘要

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.