Conventional revenue management models assume that demand parameters used for developing the control policy are already known. The optimality of the control policy, however, is often compromised when the real parameters deviate from their estimates. This article considers robust price-control for managing perishable products. It allows uncertain demand parameters whose distributions may be arbitrary. We formulate the stochastic control problem as a continuous-time model. Under fairly general conditions, we derive the optimality condition for the control policy and develop a recursive procedure for the optimal solution. We further examine structural properties of the solution and managerial insights they imply. Numerical results show that the proposed robust model outperforms the conventional benchmark model. In particular, it significantly reduces the variation in revenues without sacrificing the average revenue.
Prof. Xiao received his undergraduate degree in mathematics from Nanjing University, China, an MBA from the Catholic University of Leuven, Belgium, and a Ph.D. in Operations Research/Operations Management from the Wharton School, University of Pennsylvania. Currently he is professor and chairman of the Department of Management at C.W. Post., Long Island University. He also serves as director of the Chinese-American Research Center for Service and Operations Management at Southwest Jiaotong University, China. His research interests include revenue management, service management and optimization. He has published in Operations Research, Management Science, Mathematical Programming, European Journal of Operational Research, Decision Sciences, IEEE Transactions on Automatic Control, and other leading journals in the areas of management science and operations management.
EVENT INFO :
- Start Date:May 9, 2012
- Start Time:13:00
- End Date:May 9, 2012
- End Time:14:30
- Location:Room 221, Zaragoza Logistics Center