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管院2018年国家课题申报咨询会(二十)

2018年01月17日 14:35  点击:[]

报告主题: Analysis of Disaster-impacted Traffic Networks: A Percolation Theory Perspective

报告人:王俊伟 副教授

报告时间:2018119日下午200

报告地点:管理学院第二会议室

【专家简介】

Dr. Junwei WANG holds two PhD degrees (NEU China and University of Saskatchewan, Canada). He was a JSPS overseas research fellow at the University of Tokyo in Japan and a full professor at East China University of Science and Technology. He is currently an assistant professor in the Department of Industrial and manufacturing Systems Engineering, the University of Hong Kong. He has involved 15 research projects in Canada, Japan, Hong Kong and Mainland China as principal investigator, coordinator or project leader. He has authored or co-authored more than 50 referred journal and conference papers, and co-authored a book on optimization algorithms. His research interests include resilience engineering, service systems engineering, modeling and optimization.

【报告简介】

Disaster operations management has become a highly popular topic in OR/MS. Many assumptions are made to reduce the complexity of research, among which one frequent assumption is that the post-disaster traffic network is the same as that at pre-disaster stage. Post-disaster operations are all planned on the original traffic network, with some exceptions which used scenario-based approaches but are unable to comprehend all possible combinations and inapplicable for large-scale networks. In this talk, we examine the rationality of this assumption, and answer the questions: (1) when and to what extent can traffic networks impacted by disasters be viewed as unchanged, and (2) what is the impact of different magnitude of disasters on traffic network connectivity? A stochastic mathematical framework is proposed to investigate global connectivity and local connectivity of traffic networks affected by disasters. We also compared the impact of natural and man-made disasters on traffic networks. We discussed the application of our method to a real-world traffic network with some important insights.

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