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On the Heavy-Tail Behavior of the Distributionally Robust Newsvendor Model with Moment Constraints Natarajan, Karthik

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Since the seminal work of Scarf (1958) on the newsvendor problem with ambiguity in the demand distribution, there has been a growing interest in the study of the distributionally robust newsvendor problem. Scarf's solution is criticized at times for being overly conservative since the worst-case distribution is discrete with a few support points. A simple observation however indicates that the optimal order quantity in this two moment model is also optimal for a censored student-t distribution with infinite variance for all possible values of the critical ratio. In this paper, we generalize this ``heavy tail optimality" property of the distributionally robust newsvendor to the case when information on the first and the nth moment is known for any rational number n > 1. This is joint work with Anulekha Dhara (University of Michigan) and Bikramjit Das (SUTD).

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