Journal of Revenue and Pricing Management | November 2010
expected revenue as a function of the prices and using the stochastic gradients of the total revenue to search for a good set of prices. To compute the stochastic gradients of the total revenue, we use a novel construction that decouples the prices for the itineraries from the probability distributions of the itinerary requests. This construction ensures that the probability distributions of the underlying
random variables do not change when we change the prices for the itineraries. We establish the
convergence of our stochastic approximation algorithm. Computational experiments indicate that the prices obtained by our stochastic approximation algorithm perform significantly better than those obtained by standard benchmark strategies, especially when the leg capacities are tight and there are large differences between the price sensitivities of the different market segments.
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.
