报告主题：Train scheduling optimization with consideration of passenger flows during disturbed operations

主讲人：Andrea D'Ariano

报告时间：2026年4月17日 上午9：30 - 10：30

BOI：Andrea D'Ariano received the B.S. and M.S. degrees in Computer Science, Automation and ManagementEngineering at Roma Tre University. In November 2003, he joined TRAIL Research School and Department ofTransport and Planning, Faculty of Civil Engineering and Geosciences, Delft University of Technology. In April2008, he successfully concluded his Ph.D. studies under the supervision of Prof. Ingo A. Hansen. In 2018 and2022, he got the Full Professor Italian Scientific Habilitation in Operations Research and Transportation Science.He served as Expert and Rapporteur for European Commission and numerous national research foundations.Currently, he is working as Full Professor at Department of Civil, Computer Science and AeronauticalTechnologies Engineering, Roma Tre University. He is the coordinator of the AIRO (Italian Association of Op-erations Research) Chapter on "Optimization in Public Transport and Shared Mobility". He is Associate Editorof well-known international journals (e.g., Transportation Research Part B, C, E) and conferences (e.g., IEEEInt. Conf. on Intelligent Transportation Systems). His main research interest is the development of novel scheduling and routing methods with application topublic transportation and logistics. In SCOPUS (24/9/24), he has 153 documents, 5119 citations, 41 h-index.Andrea D'Ariano is listed in the 2024 career ranking of Top 2% of the world's best scientists (276/606 in Trans-portation & Logistics), compiled by Stanford University, DOI:10.17632/btchxktzyw.6

Abstract：Optimization models for railway traffic management tackle the problem of determining, in real-time, control actions to reduce the effect of disturbances. Two main research streams can be identified. On the one hand, train scheduling models are designed to include all conditions relevant to achieve feasible and efficient operation of rail services, keeping as much as possible train punctuality. On the other hand, delay management models focus on the impact of rescheduling decisions on the quality of service perceived by passengers. The resulting objectives are conflicting whenever train delay reduction requires cancellation of some connected services, causing extra waiting times to transferring passengers. The infrastructure manager and the train operating companies need to discuss on which connections to keep or drop.

This talk investigates hybrid railway traffic optimization approaches, merging these two streams of research. First, we consider the bi-objective problem of minimizing train delays and missed connections to provide a set of feasible non-dominated schedules, supporting the decisional process. We use a detailed alternative graph model to ensure schedule feasibility and develop heuristic algorithms to compute the Pareto front of non-dominated schedules. Second, we introduce a comprehensive mathematical model, incorporating the traffic regulations and the passenger rerouting options at a microscopic level. Third, we study this problem as a game theoretical approach, focusing on the solutions corresponding to Nash equilibria of a game involving passengers and infrastructure managers. Computational results based on a conventional Dutch railway network quantify the trade-off between the minimization of train delays and passenger travel times.

报告主题：Synchronized Transshipment: Optimizing Transfer Patterns and Schedules in Multimodal Terminals

主讲人：郭鹏副教授

报告时间：2026年4月17日 上午10：40 - 11：40

主讲人简介：郭鹏，西南交通大学机械工程学院副教授，研究方向为智能制造与物流，主持国家自然科学基金青年基金、科技部国际合作交流项目、国家科技重大专项子课题、教育部人文社科基金及四川省自然科学基金等多个项目，并参与欧盟创新框架X2Rail项目和国家重点研发计划课题等重要研究。以第一/通讯作者在IEEE TII、IJPR、TRE、Omega等国际期刊发表论文20余篇，其中2篇入选ESI高被引论文，1篇入选热点论文。担任《智能系统学报》与《工业工程》期刊青年编委，曾获得2019年Omega期刊最佳论文奖、多个会议优秀论文奖，以及2018年四川省科技进步三等奖、2021年中物联科技进步二等奖等科技奖励。

Abstract: Multimodal hub terminals are critical nodes in global supply chains, facing intense pressure to maximize throughput. A key challenge lies in coordinating the schedules of vessels, trains, and trucks with the flexible transfer patterns of containers, either via direct synchronization or intermediate storage. Contrary to conventional models that assume fixed processing times, this flexibility is a significant, yet underexploited, lever for boosting efficiency. In this study, we present an integrated optimization framework that jointly coordinates carrier schedules and container transfer patterns to maximize the number of successfully transferred containers within a finite planning horizon. We model this complex problem through two novel mixed integer programming (MIP) formulations and prove its computational complexity. To solve large-scale instances, we develop an efficient fix-and-optimize heuristic. Our computational experiments demonstrate that the proposed approach significantly outperforms conventional methods.

报告主题：Solving airlines recovery with copy and column generation

主讲人：黄周春副教授

报告时间：2026年4月17日 下午14：00 - 15：00

主讲人简介：黄周春，南京航空航天大学副教授、长空学者。本科就读于华中科技大学，美国中佛罗里达大学工业工程博士。曾任美国Sabre达拉斯总部高级运筹算法专家，从事航空领域优化模型与算法的研究。担任中国管理科学学会空天系统管理专业委员会委员，江苏省系统工程学会质量与可靠性系统工程专业委员会委员。主要研究智能优化算法设计与应用，研究方向包括随机规划、组合优化和大规模整数规划及其在能源系统优化和航空运营管理领域的应用，主持国家自然科学基金项目2项（面上1项+青年1项），以及教育部人文社科、博士后特别资助、南京市留学回国人员择优资助等项目，研究成果发表在INFORMS Journal on Computing、European Journal of Operational Research、Annal of Operations Research、Journal of Global Optimization等期刊。

报告摘要：航空公司航班计划极易受到恶劣天气、机械故障及机组人员缺勤等突发事件的影响，给航空公司和乘客带来巨大损失。航班恢复问题通过重新安排航班和调整飞机航线，尽可能快速地恢复正常运营，以应对这些干扰。将航班延误和巡航速度控制纳入恢复方案，可以生成具有调整起降时间后的航班副本，供航班恢复选择。这种方法在实际应用中存在着挑战：生成足够多的航班副本可以保证解的质量，但会增加问题规模；反之，求解速度虽能得到保证，恢复方案的质量却难以保障。我们将介绍一种新的副本与列生成（Copy and Column Generation）算法，该算法在列生成的整体框架下，利用对偶信息，融入对航班副本的评价与生成。

报告主题：Subsidy Allocation Problem with Bus Frequency Setting Game: A Tri-level Formulation and Exact Algorithm

主讲人：莫鹏里副教授

报告时间：2026年4月17日 15：00 - 16：00

主讲人简介：莫鹏里，南京航空航天大学经济与管理学院副研究员。主持国自然青年基金、江苏省青年基金等课题6项，获江苏省卓越博士后、中国运筹学会青年人才托举工程、江苏创新创业优秀博士后、CTS2023优秀博士论文等荣誉或奖励，主要研究方向为运筹优化、系统分析、仿真优化、机器学习方法在交通、物流、能源等领域的应用。在Transportation Science、Transportation Research Part B/C/E、European Journal of Operational Research、Computers & Industrial Engineering、IEEE Transactions on Intelligent Transportation Systems等海内外知名学术期刊共发表/录用论文二十余篇。

报告摘要：Typically, governments subcontract the operation of urban bus systems to several bus operators. In particular, the government aims to promote the service quality for passengers by introducing competition among bus operators and subsidizes bus operations to ensure affordable fares. However, most existing studies about subsidy allocation typically do not account for the competitive factors among bus operators and thus may underestimate the associated benefits. In this study, we investigate how the government allocates subsidies to minimize social costs, taking into account the competition among bus operators and passenger route decisions. We describe this problem as a tri-level optimization model and use a game-theoretic approach to characterize the market equilibrium of bus operators. Next, we transform the tri-level model into a mixed-integer programming problem with quadratic constraints and solve it using an exact algorithm with acceleration techniques. The results of numerical experiments demonstrate the computational efficiency of the proposed algorithm. Several valuable insights are derived: First, lines served by competing bus operators typically do not require subsidies. Second, competitive behavior decreases social costs (including bus operating costs and passenger travel costs) more effectively in cities in which the passengers assign higher value to time. Third, the competitive behavior may be guided by exogenous parameters, such as ticket prices, to approximate the optimum of urban bus systems.

报告主题：Robust dynamic train regulation in urban rail networks under disturbance uncertainties

主讲人：陈泽彬副教授

报告时间：2026年4月17日 16：00 - 17：00

主讲人简介：Zebin Chen is a Lecturer (Assistant Professor) in the Business School at Shantou University. He received his Ph.D. in Systems Science from Beijing Jiaotong University. His research focuses on urban rail transit operations, particularly real-time train scheduling/regulation and train operation control. His work has appeared in Omega and Transportation Research Part C, among other SSCI/SCI journals. He is Principal Investigator of the Young Scientists Fund (C Class) of NSFC and projects funded by the Guangdong Basic and Applied Basic Research Foundation, Guangdong Philosophy and Social Science Foundation. Dr. Chen serves as a reviewer for leading transportation journals, including Transportation Research Part B/C/E and IEEE Transactions on Intelligent Transportation Systems.

报告摘要：In dense urban rail networks with high passenger demands, uncertain disturbances occur frequently, and the resulting train delays will likely spread over the whole network rapidly, hence degrading the service quality offered to passengers. To cope with the uncertainties of frequent disturbances in urban rail networks, this paper proposes a robust train regulation strategy based on the information gap decision theory, which allows the operators to adjust the conservativeness of adjustment schemes flexibly by varying system performances but without the need for prior knowledge of uncertain disturbances. Specifically, considering the coupling relationship between train dynamic flows and passenger dynamic flows, a mixed integer quadratically constrained programming model is constructed for the robust train regulation problem to generate solutions with immunity against disturbance uncertainties, in which the envelope bound model is used to characterizing the uncertain sets of disturbances. To meet the real-time requirements of train operation adjustment, a tailored outer approximation algorithm incorporating a two-phase heuristics method is devised to effectively solve the developed robust train regulation model, thereby quickly generating high-quality solutions. Numerical experiments based on the Beijing metro network illustrate the robustness of the proposed train regulation strategies and the effectiveness of the designed solution approach.