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邀请，台湾国立东华大学Chia-li Wang教授将于2015年4月21日-26日访问我校，并作以下学术报告。
题 目： Self- vs. Social Optimization in Queueing Management
时 间： 4月22日（周三）下午 2:00—4:00
地 点： 理科楼407
摘 要：
Suppose customers arrive at an observable queuing system for service with a utility function of reward and waiting cost. The self- (customer) decision is whether to queue or balk, and the social (system administrator) goal is to maximize the profit of the whole system. While the self-optimal policy is relatively easy to obtain, the socially optimal policy, which is of more practical importance, often requires a tedious and ad hoc analysis due to the external effects.
We will introduce a simple and general approach to determine the optimal admission policy. The main idea of this approach is to consider a special rule that admits an extra customer who is served only by the surplus capacity and bears all the increased waiting time and thus incurs no external cost. The approach applies in principle to queues with exponential service. In fact, for such queues, a marginal analysis based on this rule will explore the properties of the optimal social policy and lead to a general procedure of deriving the optimal threshold. It also sheds insight into why optimal self- and social policies are different.
报告人简介：
Chia-li Wang received his M.S. degree in Stanford University and PhD. Degree in University of California, Berkeley in Operations Research respectively. Currently, he is a professor in National Dong Hwa University. His research interests includes Queuing theory, simulation, stochastic processes, operations research. He is a member in The Operations Research Society of Taiwan (ORSTW), The Chinese Institute of Probability and Statistics (CIPS), The Institute for Operations Research and the Management Sciences. He serves as referee for Journal of Applied Probability, Operations Research, Stochastic Models, Probability in the Engineering and Informational Sciences, Statistica Sinica, etc. Organizer of annual Applied Probability Workshop of Taiwan.
欢迎感兴趣的师生参加！
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2015-4-21
