Managing Inventory and Pricing with Contextual Robust Optimization

Abstract:  Multiproduct inventory and pricing problems are traditionally approached by estimating parameters for a presumed “sufficiently accurate” demand model and then optimizing with this specified demand model to determine optimal inventory and pricing decisions. However, obtaining an accurate demand model is nearly impossible due to unobservable parameters, resulting in parameter uncertainty; meanwhile, the unknown distribution of the error term in the stochastic demand model raises residual ambiguity. Additionally, the predicted demand is endogenously affected by pricing, leading to decision-dependent prediction that often brings about intractable bilinear optimization problems. We introduce a contextual robust optimization model that simultaneously addresses these challenges. Our proposed model possesses attractive finite-sample performance guarantees and can be effectively approached using an enhanced affine recourse adaptation to resolve the issue of intractability.  Extensive numerical studies demonstrate the effectiveness of our approach, showing that it outperforms the conventional estimate-then-optimize approach and the residual-based robust optimization approach that does not account for parameter uncertainty, particularly when the available data is limited. Notably, our proposed model exhibits greater resilience when contextual information is disregarded, reflecting practical situations where collecting such information might be impossible or costly.

Bio: Dr TANG Qinshen is currently an assistant professor at the Division of Information Technology and Operations Management, Nanyang Business School, Nanyang Technological University, Singapore. He joined ITOM in 2020. Prior to that, he received his PhD in Analytics and Operations from the National University of Singapore, M.S. in Management Science and Engineering, and B.S. in Industrial Engineering from the South China University of Technology. His primary research interests lie in data-driven and target-based decision-making under uncertainty, with operations and supply chain management applications. He is also interested in applying cooperative and non-cooperative game theory to solve problems at the interface of operations management and marketing/economics. His paper has been published in flagship journals such as Management Science, Operations Research, Manufacturing & Service Operations Management, etc. He is a recipient of the NBS Research Excellent Award and nominee of the NBS Teach Excellence Award.