Q-Learning-Assisted Meta-Heuristics for Scheduling Distributed Hybrid Flow Shop Problems  

作　　者：Qianyao Zhu Kaizhou Gao Wuze Huang Zhenfang Ma Adam Slowik 

机构地区：[1]Institute of Systems Engineering,Macao University of Science and Technology,Macao,99078,China [2]Department of Electronics and Computer Science,Koszalin University of Technology,Koszalin,75-453,Poland

出　　处：《Computers, Materials & Continua》2024年第9期3573-3589,共17页计算机、材料和连续体（英文）

基　　金：partially supported by the Guangdong Basic and Applied Basic Research Foundation(2023A1515011531);the National Natural Science Foundation of China under Grant 62173356;the Science and Technology Development Fund(FDCT),Macao SAR,under Grant 0019/2021/A;Zhuhai Industry-University-Research Project with Hongkong and Macao under Grant ZH22017002210014PWC;the Key Technologies for Scheduling and Optimization of Complex Distributed Manufacturing Systems(22JR10KA007).

摘　　要：The flow shop scheduling problem is important for the manufacturing industry.Effective flow shop scheduling can bring great benefits to the industry.However,there are few types of research on Distributed Hybrid Flow Shop Problems(DHFSP)by learning assisted meta-heuristics.This work addresses a DHFSP with minimizing the maximum completion time(Makespan).First,a mathematical model is developed for the concerned DHFSP.Second,four Q-learning-assisted meta-heuristics,e.g.,genetic algorithm(GA),artificial bee colony algorithm(ABC),particle swarm optimization(PSO),and differential evolution(DE),are proposed.According to the nature of DHFSP,six local search operations are designed for finding high-quality solutions in local space.Instead of randomselection,Q-learning assists meta-heuristics in choosing the appropriate local search operations during iterations.Finally,based on 60 cases,comprehensive numerical experiments are conducted to assess the effectiveness of the proposed algorithms.The experimental results and discussions prove that using Q-learning to select appropriate local search operations is more effective than the random strategy.To verify the competitiveness of the Q-learning assistedmeta-heuristics,they are compared with the improved iterated greedy algorithm(IIG),which is also for solving DHFSP.The Friedman test is executed on the results by five algorithms.It is concluded that the performance of four Q-learning-assisted meta-heuristics are better than IIG,and the Q-learning-assisted PSO shows the best competitiveness.

关 键 词：Distributed scheduling hybrid flow shop META-HEURISTICS local search Q-LEARNING 

分 类 号：TH186[机械工程—机械制造及自动化]