Robust Pricing and Production with Information Partitioning and Adaptation

Georgia Perakis (), Melvyn Sim (), Qinshen Tang () and Peng Xiong ()
Additional contact information
Georgia Perakis: Sloan School of Management, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139
Melvyn Sim: Department of Analytics & Operations, NUS Business School, National University of Singapore, Singapore 119077
Qinshen Tang: Division of Information Technology & Operations Management, Nanyang Business School, Nanyang Technological University, Singapore 639798
Peng Xiong: Department of Analytics & Operations, NUS Business School, National University of Singapore, Singapore 119077

Management Science, 2023, vol. 69, issue 3, 1398-1419

Abstract: We introduce a new distributionally robust optimization model to address a two-period, multiitem joint pricing and production problem, which can be implemented in a data-driven setting using historical demand and side information pertinent to the prediction of demands. Starting from an additive demand model, we introduce a new partitioned-moment-based ambiguity set to characterize its residuals, which also determines how the second-period demand would evolve from the first-period information in a data-driven setting. We investigate the joint pricing and production problem by proposing a cluster-adapted markdown policy and an affine recourse adaptation, which allow us to reformulate the problem as a mixed-integer linear optimization problem that we can solve to optimality using commercial solvers. We also extend our framework to ensemble methods using a set of ambiguity sets constructed from different clustering approaches. Both the numerical experiments and case study demonstrate the benefits of the cluster-adapted markdown policy and the partitioned moment-based ambiguity set in improving the mean profit over the empirical model—when applied to most out-of-sample tests.

Keywords: multiitem; pricing; retail analytics; clustering; distributionally robust optimization (search for similar items in EconPapers)
Date: 2023
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Citations: View citations in EconPapers (2)

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