On the Heavy-Tail Behavior of the Distributionally Robust Newsvendor

Abstract

Distributionally robust optimization is increasingly becoming a popular methodology to deal with uncertainty in optimization problems. Although the methodology optimizes for the worst-case distribution, a better understanding of the prescriptions from the models important from a practical perspective. In the paper “On the heavy-tail behavior of the distributionally robust newsvendor,” B. Das, A. Dhara, and K. Natarajan provide a novel analysis for the problem in the newsvendor setting with moment information. They show that the distributionally robust newsvendor by planning for the worst possible demand distribution with moment information will remains optimal if the true demand distribution is heavy-tailed. The prescribed optimal solution has a heavy-tail optimality’ property for free.