Date/Time: Tuesday, December 6 at 1:10-2:00 PM
Location: 303 Mudd
Abstract: In this talk, we discuss pricing problems under the Markov chain choice model. In this choice model, a customer arrives into the system with an interest to purchase a particular product. After checking the price of this product, the customer decides whether to purchase the product. If she decides not to purchase this product, then the customer transitions to another product or to the no-purchase option according to a certain transition probability matrix. If the customer transitions to another product, then she checks the price of the next product. In this way, the customer transitions between the products until she purchases a product or transitions into the no-purchase option. We discuss four classes of pricing problems. First, we discuss static pricing problems, where the goal is to find the prices to charge for the products to maximize the expected revenue obtained from a customer. We show how to compute the optimal prices tractably. Second, we discuss dynamic pricing problems with a single resource, where we offer multiple products by using a single resource and the sale of a product consumes the inventory of the resource. We characterize structural properties of the optimal policy. Third, we discuss dynamic pricing problems over a network of resources, where we offer multiple products by using a network of resources and the sale of a product consumes a combination of resources. A standard fluid approximation to the problem is a non-convex program. We give an equivalent convex formulation. Fourth, we discuss competitive pricing problems under the Markov chainchoice model. Lastly, we touch up on how to estimate the parameters of the Markov chain choice model. This is joint work with James Dong and Serdar Simsek.
Bio: Huseyin Topaloglu is a professor in the School of Operations Research and Information Engineering at Cornell Tech. He holds a B.Sc. in Industrial Engineering from Bogazici University in Turkey, and a Ph.D. in Operations Research and Financial Engineering from Princeton University. His research interests include stochastic programming and approximate dynamic programming with applications in transportation logistics, revenue management and supply chain management. His recent work focuses on constructing tractable solution methods for large-scale network revenue management problems and building approximation strategies for retail assortment planning. Huseyin Topaloglu is currently serving as the department editor for the Revenue Management Department at Production and Operations Management and associate editor for Operations Research, Management Science, Naval Research Logistics, Transportation Science, Mathematical Programming Computation and IIE Transactions.