A data-driven distributionally robust newsvendor model with a Wasserstein ambiguity set. Issue 8 (3rd August 2021)

• Record Type: Journal Article
• Authors: Lee, Sangyoon; Kim, Hyunwoo; Moon, Ilkyeong
• Is Part Of: Journal of the Operational Research Society. Volume 72:Issue 8(2021)
• Page Start: 1879
• Page End: 1897
• Publication Date: 2021-08-03
• Subjects: Newsvendor Model -- distributionally robust optimization -- Wasserstein distance; Operations research -- Periodicals; 658.4034
• DOI: 10.1080/01605682.2020.1746203
• Languages: English
• ISSNs: 0160-5682

Abstract

Abstract: In this paper, we derive a closed-form solution and an explicit characterization of the worst-case distribution for the data-driven distributionally robust newsvendor model with an ambiguity set based on the Wasserstein distance of order p ∈ [ 1, ∞ ) . We also consider the risk-averse decision with the Conditional Value-at-Risk (CVaR) objective. For the risk-averse model, we derive a closed-form solution for the p = 1 case, and propose a tractable formulation to obtain an optimal order quantity for the p > 1 case. We conduct numerical experiments to compare out-of-sample performance and convergence results of the proposed solutions against the solutions with other distributionally robust models. We also analyze the risk-averse solutions compared to the risk-neutral solutions.