转载自：智能调度与组合优化公众号~~~
原文链接放在阅读原文中
为推广演化计算在调度与组合优化领域的研究，IEEE演化调度与组合优化Taskforce在此组织线上学术报告讲座系列。
以下为第7期讲座内容：
题目：学习优化车辆路径规划问题（Learning to Solve Vehicle Routing Problems）
主讲人：Zhiguang Cao（Singapore Institute of Manufacturing Technology (SIMTech), Singapore）
主持人：梅一（Senior Lecturer, Victoria University of Wellington, NZ)
时间：2022年11月9日 下午4:00 - 5:00（北京时间）
讲座语言：英文
主办单位：IEEE Taskforce on Evolutionary Scheduling and Combinatorial Optimisation (https://homepages.ecs.vuw.ac.nz/~yimei/ieee-tf-esco/)
Zoom link: https://vuw.zoom.us/j/91711326799
主讲人简介
Dr. Zhiguang Cao is currently a Scientist at Singapore Institute of Manufacturing Technology (SIMTech), Agency for Science Technology and Research (A*STAR). Previously he was a Research Assistant Processor in Department of Industrial Systems Engineering and Management, National University of Singapore (NUS). In recent years, his research interests focus on Learning to Optimize, where he exploited deep (reinforcement) learning to solve Combinatorial Optimization Problems, such as Vehicle Routing Problem, Job Shop Scheduling Problem, Bin Packing Problem and Integer Programs. It is a hot yet challenging topic in both AI and OR. His works under this topic are published in NeurIPS, ICLR, AAAI, IJCAI and IEEE Trans, and the papers & codes are available at: https://zhiguangcaosg.github.io/publications/.
个人学术主页：https://zhiguangcaosg.github.io/
报告摘要
Vehicle routing problem (VRP) is the most widely studied problem in operations research (OR), which is always solved using heuristics with hand-crafted rules. In recent years, there is a growing trend towards exploiting deep (reinforcement) learning to automatically discover a heuristic or rule for solving VRPs. In this talk, I will first briefly introduce the construction type of neural methods, followed by the elaboration of improvement type. Then, I will present the challenges in this area and my personal thoughts on them.
如有任何问题，请联系梅一(yi.mei@ecs.vuw.ac.nz)或张芳芳(fangfang.zhang@ecs.vuw.ac.nz)。
