A data-driven distributionally robust newsvendor model with a Wasserstein ambiguity set

Sangyoon Lee, Hyunwoo Kim and Ilkyeong Moon

Journal of the Operational Research Society, 2021, vol. 72, issue 8, 1879-1897

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.

Date: 2021
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Citations: View citations in EconPapers (6)

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DOI: 10.1080/01605682.2020.1746203

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