Abstract

We consider a revenue management problem that arises from the selling of high-speed train tickets in China. Compared with traditional network revenue management problems, the new feature of our problem is the assign-to-seat restriction. That is, each request, if accepted, must be assigned instantly to a single seat throughout the whole journey, and later adjustment is not allowed. When making decisions, the seller needs to track not only the total seat capacity available but also the status of each seat. We build a modified network revenue management model for this problem. First, we study a static problem in which all requests are given. Although the problem is NP-hard in general, we identify conditions for solvability in polynomial time. We then introduce a bid-price control policy based on a novel maximal sequence principle. This policy accommodates nonlinearity in bid prices and, as a result, yields a more accurate approximation of the value function than a traditional bid-price control policy does. Finally, we combine a dynamic view of the maximal sequence with the static solution of a primal problem to propose a "re-solving a dynamic primal" policy that can achieve uniformly bounded revenue loss under mild assumptions. Numerical experiments using both synthetic and real data document the advantage of the policies we propose for improving the efficiency of capacity allocation.

About the Speaker

Prof. Zizhuo Wang is a Professor and Associate Dean at the School of Data Science. He is also the co-founder and CTO of Cardinal Operations (杉数科技). He obtained his bachelor's degree in Mathematics from Tsinghua University in 2007, and his Ph.D. degree in Operations Research from Stanford University in 2012. Prior to joining CUHK-Shenzhen, he was an Associate Professor (with tenure) in the Department of Industrial and Systems Engineering at the University of Minnesota.

His research interests mainly focus on optimization and stochastic modeling, especially with applications to pricing and revenue management. He has published over 50 papers in top journal in the field of operations research and management science, and has been the Associate Editors or Senior Editors for the top journals such as Management Science, Operations Research, MSOM and POMS. His research has been supported by the National Natural Science Foundation of China (NSFC), the National Science Foundation (NSF) in the United States and other funding agencies, with a total amount of near 10M RMB.

Zizhuo Wang has extensive experiences in applying data-driven methods in industry. In 2016, he co-founded Cardinal Operations with others, which served over 200 enterprises to provide data-driven decision support service and products.