## 新闻中心NEWS

### 讲座：Dynamic Pricing and Matching for Two-Sided Queues

#### 发布者：管理科学系    发布时间：2020-04-16

Motivated by diverse applications in sharing economy and online marketplaces, we consider optimal pricing and matching control in a two-sided queueing system.  We assume that heterogeneous customers and servers arrive to the system with price-dependent arrival rates. The compatibility between servers and customers is specified by a bipartite graph. Once a pair of customer and server are matched, they depart from the system instantaneously. The objective is to maximize long-run average profits of the system while minimizing average waiting time. We first propose a static pricing and max-weight matching policy, which achieves $O(\sqrt{\eta})$ optimality rate when all of the arrival rates are scaled by $\eta$. We further show that a dynamic pricing and modified max-weight matching policy achieves an improved $O(\eta^{1/3})$ optimality rate. Under a broad class of pricing policies, we prove that any matching policy has an optimality rate that is lower bounded by $\Omega(\eta^{1/3})$. Thus, the dynamic pricing policy and modified max-weight matching policy achieves the optimal rate.  In addition, we propose a constraint generation algorithm that solves an approximation of the MDP and demonstrate strong numerical performance of this algorithm.

He Wang is an assistant professor in the School of Industrial and Systems Engineering at Georgia Tech. His research interest is in revenue management, supply chain and logistics, and statistical learning. His recent research focuses on developing data-driven methods for the interface between machine learning and operations management. He received his Ph.D. in Operations Research and M.S. in Transportation at MIT, and his B.S. in Industrial Engineering from Tsinghua University. His works have been awarded in INFORMS JFIG paper competition and MSOM student paper competition. He was selected as a Class of 1969 Teaching Fellow at Georgia Tech.