编辑:吴秦时间:2019-12-16点击数: 来源:  

报告题目(Title): Evolving Scheduling Heuristics for Dynamic Flexible Job Shop Scheduling with Genetic Programming

报告人姓名(Speaker):  Dr. Yi Mei, Evolutionary Computation Research Group, School of Engineering and Computer Science, Victoria University of Wellington

时间(Date & Time):  1217  15:30-16:30

地点(Location):  jdb电子游戏C529会议室

报告摘要(Abstract): Flexible Job Shop Scheduling (FJSS) is an important but challenging problem with many applications in the real world. Compared with traditional JSS, FJSS requires to make to types of decisions – routing and sequencing. The routing decision (for a job operation) allocates each operation of a job to the best machine, and the sequencing decision (for a machine) decides which operation in the queue is to be processed next by the machine. In dynamic FJSS, jobs arrive in real time and the schedule needs to be adjust accordingly. Traditional solution optimisation approaches are not effective in this case due to high complexity. Following the idea of data-driven optimisation and machine learning, Genetic Programming (GP) is promising to evolve scheduling heuristics (e.g. rules) offline, which can be applied online to make decisions in real time. This talk will introduce our works on developing novel GP algorithms to evolve scheduling heuristics effectively and efficiently. This will include solution representation, evaluation, and search process.


Dr. Yi Mei is a Senior Lecturer at the School of Engineering and Computer Science, Victoria University of Wellington, Wellington, New Zealand. His research interests include evolutionary scheduling and combinatorial optimisation, machine learning, genetic programming, and hyper-heuristics. He has more than 100 fully referred publications, including the top journals in EC and Operations Research such as IEEE TEVC, IEEE TCYB, Evolutionary Computation Journal, European Journal of Operational Research, ACM Transactions on Mathematical Software. He received an IEEE Transactions on Evolutionary Computation (top journal in evolutionary computation) Outstanding Paper Award in 2017. He serves as a Vice-Chair of the IEEE CIS Emergent Technologies Technical Committee, and a member of Intelligent Systems Applications Technical Committee. He is an Editorial Board Member of International Journal of Bio-Inspired Computation, and a guest editor of a special issue of the Genetic Programming Evolvable Machine journal. He serves as a reviewer of over 30 international journals. He is an IEEE Senior Member.