报告题目：Bayesian approach to deal with incomplete information for queueing models
报告人：赵以强教授
报告时间：10月25日星期四 下午14:00
报告地点：尚贤楼706
主持人：张建伟教授
专家简介：赵以强博士目前是加拿大卡尔顿大学理学院副院长（主管科研与研究生）、终身教授，他82年（77级）毕业于南京气象学院数学师资班，后留校任教，86年去加拿大留学，就读于萨斯卡彻温大学数学与统计系，90年获博士学位，90-92年在加拿大皇后大学加拿大通讯研究所从事博士后研究，92-2000年在加拿大威尼伯大学数学统计系先后任助理教授和副教授，2000年至今在加拿大卡尔顿大学数学与统计学院任教授。
赵教授主要从事应用概率和随机过程的研究，在国际一流权威期刊上发表了大量高水平的学术论文，其研究成果产生了重大和深远的影响，被国际同行广泛引用，并得到高度评价，在应用概率，尤其是排队论及相关领域做出了卓越的贡献，担任五种国际权威期刊，包括OR Letters、Queueing Systems、Stochastic Models，并兼任多种学术期刊的审稿人。
报告摘要：In this talk, we describe challenges in modeling and analyzing queueing systems with incomplete information, which is often the case arising from large-scaled systems with dependence such as various Internet applications involving big data. We try to use Bayesian approach for queueing models with miss information. We first start with a simple example to demonstrate basic ideas and concepts and then show how Bayesian approach works for the join-the-shortest-queue (JSQ for short) model with two parallel queues, where the servers may be heterogeneous. Since in practice, the complete data are not always (or seldom) available, we are dealing with the uncertainty of the JSQ parameters in case of missing information. The uncertainty is, indeed, caused by the lack of information (to the observer or customers) about the desired queueing model, resulted in situations where the shortest queue is not always assigned (by the system) to the upcoming jobs. We show how a Bayesian approach can be applied to estimate unknown system parameters and to how the prediction can help the system to assign the shortest queue to the upcoming jobs more accurately. This talk is based on the joint work with Ehssan Ghashim.
数学与统计学院
2018年10月23日