| 报告人 |
余浩 挪威特罗姆瑟北极大学工业工程副教授兼项目负责人 Hao Yu is Associate Professor and Program Leader of Industrial Engineering (MSc) at UiT The Arctic University of Norway. His research interests are real-problem-driven model development to solve complex decision-making problems in sustainable supply chains, reverse logistics, location and network design, service systems, scheduling, and smart manufacturing and logistics in Industry 4.0/5.0. These optimization/simulation models can be used to minimize carbon footprints and environmental risks of logistics systems, improve the accessibility and cost efficiency of service networks, locate the charging stations for electric/hydrogen vehicles, autonomously configure reconfigurable manufacturing systems, improve the lockage efficiency of a water conservancy project, and so forth. He has led or been a key member of 10+ international projects funded by the Research Council of Norway, the Directorate of Higher Education and Skills, Innovation Norway, Nordplus, and EU and EEA Programmes. He has authored and co-authored 50+ scientific papers with 1500+ citations and 18 H-Index. Currently, his research focuses on the combination of predictive analytics, prescriptive analytics, and descriptive analytics in an Industry 5.0-enabled Smart Digital Logistics Twin that can be used in, for example, reverse logistics, humanitarian/epidemic logistics, and smart manufacturing/remanufacturing systems. |
内容简介 |
Introduction: Optimization and simulation are the most important analytical techniques, and this talk will provide a concise guide to optimization vs. simulation in decision-making. Specifically, we will start with an urban postal service redesign problem in Norway. Due to recent technological advancements, more diversified customer demand, and increasingly harder competition, a strategic reform of urban postal service systems was undertaken in Norway in 2013, called post-in-shop. Decisions need to be made to replace traditional post offices with small-sized postal service counters in retail stores to improve accessibility, operational efficiency, and cost-effectiveness. To solve this problem, a two-stage method is proposed. First, two location models are employed to determine the optimal facility locations, and a simulation model is then built to evaluate the urban postal service system with different location and demand allocation plans under a realistic and stochastic environment. This case study will show both the strengths and the weaknesses of optimization and simulation and how they can be complementarily applied in decision-making. Furthermore, some other application scenarios using combined simulation and optimization methods, e.g., waste management, vaccine distribution, and smart manufacturing, will be briefly introduced. |
