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【学术通知】上海交通大学董浩云智能制造与服务管理研究院助理研究员夏。篧eekly rolling stock planning in Chinese high-speed rail networks

  • 发布日期:2022-12-13
  • 点击数:

  

喻园管理论坛2022年第53期(总第822期)

演讲主题: Weekly rolling stock planning in Chinese high-speed rail networks

主 讲 人: 夏。虾=煌ù笱Ф圃浦悄苤圃煊敕务管理研究院助理研究员

主 持 人: 秦虎,爱游戏体育官方赞助罗马02管理科学与信息管理系教授

活动时间2022年12月14日(周三)14:00-15:30

活动地点线上腾讯会议ID: 725 381 191

主讲人简介:

Jun Xia is currently an Assistant professor at C.Y. Tung Institute of Intelligent Manufacturing and Service Management, Shanghai Jiao Tong University. He obtained his PhD degree from Department of Logistics and Maritime Studies, Hong Kong Polytechnic University. His research is mainly focused on optimization in transportation and logistics. His research works were published in leading journals such as Transportation Science, Transportation Research Part B, Naval Research Logistics and etc.

活动简介:

In high-speed rail networks, train units are scheduled to periodically meet all maintenance requirements while at the same time continuing to serve all scheduled passenger trips. Motivated by the trip demand variances on the days of every week in China, this paper studies a weekly rolling stock planning (W-RSP) problem that aims to optimize the rotation plan for the train units on each day of a week, so as to minimize their operating cost, including any (un)coupling costs and maintenance costs. We model the W-RSP on a newly developed rotation network by adopting particular nodes and arcs to address the (un)coupling operations of train units, and then propose an integer linear programming formulation for the problem. To solve this formulation, we develop a customized branch-and-price algorithm, which relies on a reduced linear programming relaxation for computing the lower bound, embeds a diving algorithm for computing the upper bound, and integrates advanced branching rules for effective explorations of the solution space. Computational results validate the effectiveness and efficiency of the proposed solution algorithm, which is able to solve large instances with up to 5034 trips to near-optimality.

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