Journal of Revenue and Pricing Management | May 2011
method approximates the optimal total expected revenue arbitrarily closely in an asymptotic regime where the leg capacities and the number of time periods in the decision horizon increase linearly with the same rate. Numerical experiments indicate that our approach may be a viable alternative to the standard deterministic linear program that appears in the existing literature.
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.
