Operations Research | 2008
are known to be optimal and we propose stochastic approximation methods to compute the optimal base-stock levels. The existing stochastic approximation methods in the literature guarantee that their iterates converge, but not necessarily to the optimal base-stock levels. In contrast, we prove
that the iterates of our methods converge to the optimal base-stock levels. Moreover, our methods
continue to enjoy the well-known advantages of the existing stochastic approximation methods. In particular, they only require the ability to obtain samples of the demand random variables, rather than to compute expectations explicitly and they are applicable even when the demand information is censored by the amount of available inventory.
Sumit Kunnumkal is a Professor and Area Leader of Operations Management at the Indian School of Business (ISB). He holds a PhD in Operations Research from Cornell University. He received his MS in Transportation from the Massachusetts Institute of Technology and a B.Tech in Civil Engineering from the Indian Institute of Technology, Madras.
Professor Kunnumkal has previously taught at the Smith School of Business, Queen’s University, and has held visiting positions at the Singapore University of Technology and Design and Universitat Pompeu Fabra. His research interests lie in the areas of pricing and revenue management, retail operations, assortment planning, and approximate dynamic programming.
At ISB, he has taught in the PGP programme, the Fellow programme, and various Advanced Management and Executive Education programmes.
