讲座题目:

   1. Capacity expansion strategies for electric vehicle charging networks: Model, algorithms, and case study

   2. Modeling the impacts of uncertain carbon tax policy on maritime fleet mix strategy and carbon mitigation

   3.学术论文撰写中注意问题

讲 座 人：加拿大麦克马斯特大学黄楷教授；加拿大约克大学陈圣元教授

讲座地点：管理楼A515

讲座时间：2024年7月9日下午14:00-17:00

欢迎广大师生参与！

讲座题目1摘要：

Governments in many jurisdictions are taking measures to promote the use of electric vehicles. As part of this goal, it is crucial to provide a sufficient number of charging stations to alleviate drivers’ anxieties associated with the range of the vehicle. The goal of this research is to help governments develop vehicle charging networks for public use via the application of multistage stochastic integer programming model that determines both the locations and capacities of charging facilities over finite planning horizons. The logit choice model is used to estimate drivers’ choices of nearby charging stations. Moreover, we characterize the charging demand as a function of the charging station quantity to reflect the range anxiety of consumers. The objective of the model is to minimize the expected total cost of installing and operating the charging facilities. An approximation algorithm, a heuristic algorithm, and a branch-and-price algorithm are designed to solve the model. We conduct numerical experiments to test the efficiency of these algorithms. Importantly, each algorithm has advantages over the CPLEX MIP solver. Finally, the City of Oakville in Ontario, Canada, is used to demonstrate the effectiveness of this model.

讲座题目2摘要：

The maritime transport industry continues to draw international attention on significant Greenhouse Gas emissions. The introduction of emissions taxes aims to control and reduce emissions. The uncertainty of carbon tax policy affects shipping companies’ fleet planning and increases costs. We formulate the fleet planning problem under carbon tax policy uncertainty a multi-stage stochastic integer-programming model for the liner shipping companies. We develop a scenario tree to represent the structure of the carbon tax stochastic dynamics, and seek the optimal planning, which is adaptive to the policy uncertainty. Non-anticipativity constraint is applied to ensure the feasibility of the decisions in the dynamic environment. For the sake of comparison, the Perfect Information (PI) model is introduced as well. Based on a liner shipping application of our model, we find that under the policy uncertainty, companies charter more ships when exposed to high carbon tax risk, and spend more on fleet operation; meanwhile the CO2 emission volume will be reduced.

报告人黄楷简历:

Dr. Kai Huang is a Professor of Operations Management at DeGroote School of Business, McMaster University. Dr. Huang obtained his Bachelor degree in Huazhong University of Science and Technology, Master degree in Tsinghua University, and PhD degree in School of Industrial and Systems Engineering, Georgia Institute of Technology. Dr. Huang specializes in data-driven optimization techniques with applications in business analytics and supply chain management. His work appeared in Operations Research, Naval Research Logistics, Mathematical Programming, etc. His recent research interests include data-driven inventory management, long-term care system design, humanitarian logistics, electric vehicles infrastructure design, and closed-loop supply chain management. His research is supported by Natural Sciences and Engineering Research Council of Canada, Social Sciences and Humanities Research Council of Canada, National Research Council Canada, etc. He was recognized as the recipient of 2024 Research Excellence Award at DeGroote School of Business, McMaster University.

报告人陈圣元简历:

Dr. Michael Chen is an Associate Professor in the Department of Mathematics and Statistics at York University since 2009. He obtained his Bachelor degree in Jilin University, Master degree in University of British Columbia, and PhD degree in Industrial Engineering and Management Science, Northwestern University. Prior to teaching at York, he did his post-doctorate at the prestigious IBM T.J. Watson Research Centre in New York. His research Area is in applied mathematics. His research interests include Business Analytics, Data Mining/Deep Learning, Large Scale Optimization, Stochastic Optimization and AI in Healthcare/Education/Finance/Supply Chain.