时 间：11月10日下午14:00
地 点：九里校区零号楼0411室
题 目：城市地铁网络列车运行计划快速编制优化方法
内 容：With increased service lines and stations in large urban rail networks, there are invariably large passenger flows that involve transfers between lines, and the passenger demand can vary significantly between stations and over time of the day. Carefully coordinating train timetables of different operating lines can help reduce transfer delays, which in turn reduces station crowding and improves overall service quality. In this presentation, we explore the train timetable coordination optimization problem that aims to minimize the passenger waiting, transfer time and station crowding, and maximize the number of effective connections among different lines. The problem is formulated as a mixed integer nonlinear programming model. To effectively address the complexity of our model, a decomposition and approximate dynamic programming approach is designed to reformulate the original network-level problem into many small-scale subproblems, to be solved quickly in a distributed manner. The effectiveness and practicability of the model and method are demonstrated on two case networks: a simple synthetic network of three metro lines and a real network based on Beijing Subway. The computational results illustrate that our proposed strategies can effectively reduce passenger waiting time and station crowing, our proposed decomposition and approximate dynamic programming approach is also shown to perform more efficiently than traditional centralized heuristic algorithms, especially for larger-scale networks.
主讲人简介：李树凯，北京交通大学系统科学学院教授，博士生导师，国家优秀青年科学基金获得者。兼任中国运筹学会智能计算分会副理事长、中国运筹学会不确定系统分会理事、管理科学与工程学会交通运输管理分会委员会委员等。在Transportation Research Part B/C/E、Automatica、IEEE ITS/CST/SMC/TVT等国际顶级/重要学术期刊发表SCI检索论文70余篇。成果获中国城市轨道交通协会科技进步一等奖1项，中国自动化学会自然科学二等奖1项。申请国家发明专利20余项，其中授权8项。主持在研/完成国家自然科学基金项目3项、北京市自然科学基金项目3项，作为核心骨干参与国家重点研发计划项目2项。担任国际SCI期刊《Computers & Industrial Engineering》客座编委和国内EI期刊《铁道科学与工程学报》青年编委等。主要研究方向：城市交通系统建模与优化控制、轨道交通运输组织与管理、城市公共交通协同优化决策。
主办：经济管理学院 运营管理创新团队
承办：服务科学与创新四川省重点实验室
