IEEE ESCO Taskforce Webinar #7: Learning to Solve Vehicle Routing Problems

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From: yi...@ecs.vuw.ac.nz <yi...@ecs.vuw.ac.nz>
Sent: Tuesday, November 8, 2022 2:03 AM
Subject: [EXT] IEEE ESCO Taskforce Webinar #7: Learning to Solve Vehicle Routing Problems

 

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Dear colleagues,

We (IEEE CIS Taskforce on Evolutionary Scheduling and Combinatorial Optimisation) are organising Webinars on evolutionary computation and combinatorial optimisation, and our next Webinar is on 9th November (TOMORROW), 2022, by Dr. Zhiguang Cao from Singapore Institute of Manufacturing Technology (SIMTech), Agency for Science Technology and Research (A*STAR), Singapore.

We sincerely invite you to join the Webinar, and circulate to your colleagues and invite them to join. The information of the Webinar is as follows:

Webinar #7: Learning to Solve Vehicle Routing Problems

Speaker: Zhiguang Cao, Scientist, Singapore Institute of Manufacturing Technology (SIMTech), Agency for Science Technology and Research (A*STAR), Singapore
Date: 9 November 2022
Time: 4:00 - 5:00pm (China Time, UTC+8) [Convert to your local time]
Zoom link: https://vuw.zoom.us/j/91711326799

Speaker Biography

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/.

Abstract

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.

If you have any questions or queries, please email Yi Mei or Fangfang Zhang.

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Dr. Yi Mei, SMIEEE, MACM
Chair of IEEE CIS Taskforce on Evolutionary Scheduling and Combinatorial Optimisation
Senior Lecturer
Evolutionary Computation Research Group
School of Engineering and Computer Science
Victoria University of Wellington
PO Box 600, Wellington 6140
New Zealand
Homepage: http://meiyi1986.github.io/