演讲主题: Multi-stage stochastic programing approach for network capacity expansion: Models and algorithms

　　演讲主题: Multi-stage stochastic programing approach for network capacity expansion: Models and algorithms

　　主 讲 人: Kai Huang，Degroote School of Business, McMaster University

　　主 持 人: 物流系 邓世名教授

　　活动时间: 2016年5月13日14:30-16:30

　　活动地点: 管理学院130教室

　　主讲人简介：Dr. Huang specializes in operations research and supply chain management. His recent research interests include logistics network capacity expansion, inventory management, humanitarian logistics, and food safety. He teaches courses in operations research and supply chain management. Dr. Huang’s research work has appeared in refereed articles published in journals such as Operations Research, European Journal of Operational Research, Naval Research Logistics, and Operations Research Letters. Dr. Huang’s research is supported by the Social Sciences and Humanities Research Council (SSHRC) and Natural Science and Engineering Research (NSERC).

　　Dr. Fu has provided advisory and economic modeling services to organizations such as the Boeing Commercial Aircraft, New Zealand Commerce Commission, Australian Competition and Consumer Commission, , Government of British Columbia in Canada, Australian Competition Tribunal, Hong Kong Central Policy Unit, Japan Rail (East), Hong Kong Civil Aviation Department, and OECD. He is a member of the Air Transport Research Society (ATRS), International Maritime Economists Association (IAME), the World Conference of Transportation Research (WCTR) and the American Economics Association.

　　摘要：

　　In networks, there are often more than one source of capacity. The capacities can be permanently or temporarily owned by the decision maker. Depending on the nature of sources, we identify the permanent capacity, spot market capacity and contract capacity. We use a scenario tree to model the uncertainty, and build a multi-stage stochastic integer program that can incorporate multiple sources and multiple types of capacities in a general network. We propose two solution methodologies for the problem. Firstly, we design an asymptotically convergent approximation algorithm. Secondly, we design a cutting plane algorithm based on Benders decomposition to find tight bounds for the problem. The numerical experiments show superb performance of the proposed algorithms compared with commercial software.

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