国产丝袜极品视频在线观看_午夜精品一区二区成人免费_色偷偷亚洲男人天堂岛_亚洲AV自慰白浆喷水肥臀_中文在线理伦视频在线播放

ENGLISH
您所在的位置: 首頁» 新聞中心» 講座預(yù)告

【明理講堂2020年第29期】11-9香港理工大學(xué)宋苗副教授:A General Model and Efficient Algorithms for Reliable Facility Location Problem under Uncertain Disruptions

  時  間:11月9日下午15:30-17:00

  會議號:騰訊會議 498 951 790

  報告人:香港理工大學(xué)宋苗副教授

  報告題目:A General Model and Efficient Algorithms for Reliable Facility Location Problem under Uncertain Disruptions

  報告內(nèi)容摘要:

  This paper studies the reliable uncapacitated facility location problem in which facilities are subject to uncertain disruptions. A two-stage distributionally robust model is formulated, which optimizes the facility location decisions so as to minimize the fixed facility location cost and the expected transportation cost of serving customers under the worst-case disruption distribution. The model is formulated in a general form, where the uncertain joint distribution of disruptions is partially characterized and is allowed to have any pre-specified dependency structure. This model extends several related models in the literature, including the stochastic one with explicitly given disruption distribution and the robust one with moment information on disruptions. An efficient cutting plane algorithm is proposed to solve this model, where the separation problem is solved respectively by a polynomial-time algorithm in the stochastic case and by a column generation approach in the robust case. Extensive numerical study shows that the proposed cutting plane algorithm not only outperforms the best-known algorithm in the literature for the stochastic problem under independent disruptions but also efficiently solves the robust problem under correlated disruptions. The practical performance of the robust models is verified in a simulation based on historical typhoon data in China. The numerical results further indicate that the robust model with even a small amount of information on disruption correlation can mitigate the conservativeness and improve the location decision significantly.  

  報告人簡介: 

  Dr. Miao Song is an associate professor in the Department of Logistics and Maritime Studies at the Hong Kong Polytechnic University. Before joining the Hong Kong Polytechnic University, she got her PhD degree from MIT and served as an assistant professor at the Department of Industrial and Manufacturing Systems Engineering at the University of Hong Kong. Her research focuses on applications of optimization methods in operations management, particularly inventory and network design problems. Her research works have been published in top journals such as Operations Research, Management Science, INFORMS Journal on Computing, and Production and Operations Management.

  

 ?。ǔ修k:管理工程系、科研與學(xué)術(shù)交流中心)

TOP