A Q-Learning Based Hybrid Meta-Heuristic for Integrated Scheduling of Disassembly and Reprocessing Processes Considering Product Structures and Stochasticity  

作　　者：Fuquan Wang Yaping Fu Kaizhou Gao Yaoxin Wu Song Gao 

机构地区：[1]the School of Business,Qingdao University,Qingdao 266071,China [2]the Macao Institute of Systems Engineering,Macao University of Science and Technology,Macao 999078,China [3]the Department of Industrial Engineering&Innovation Sciences,Eindhoven University of Technology,Eindhoven,5600 MB,the Netherlands [4]the College of Information Science and Engineering,Northeastern University,Shenyang 110819,China

出　　处：《Complex System Modeling and Simulation》2024年第2期184-209,共26页复杂系统建模与仿真（英文）

基　　金：This work was in part supported by the Science and Technology Development Fund(FDCT),Macao SAR,(No.0019/2021/A);Shandong Province Outstanding Youth Innovation Team Project of Colleges and Universities(No.2020RWG011),National Natural Science Foundation of China(Nos.62173356 and 61703320);Natural Science Foundation of Shandong Province(No.ZR202111110025);Guangdong Basic and Applied Basic Research Foundation(No.2023A1515011531);Zhuhai Industry-University-Research Project with Hongkong and Macao(No.ZH22017002210014PWC).

摘　　要：Remanufacturing is regarded as a sustainable manufacturing paradigm of energy conservation and environment protection.To improve the efficiency of the remanufacturing process,this work investigates an integrated scheduling problem for disassembly and reprocessing in a remanufacturing process,where product structures and uncertainty are taken into account.First,a stochastic programming model is developed to minimize the maximum completion time(makespan).Second,a Q-learning based hybrid meta-heuristic(Q-HMH)is specially devised.In each iteration,a Q-learning method is employed to adaptively choose a premium algorithm from four candidate ones,including genetic algorithm(GA),artificial bee colony(ABC),shuffled frog-leaping algorithm(SFLA),and simulated annealing(SA)methods.At last,simulation experiments are carried out by using sixteen instances with different scales,and three state-of-the-art algorithms in literature and an exact solver CPLEX are chosen for comparisons.By analyzing the results with the average relative percentage deviation(RPD)metric,we find that Q-HMH outperforms its rivals by 9.79%-26.76%.The results and comparisons verify the excellent competitiveness of Q-HMH for solving the concerned problems.

关 键 词：remanufacturing scheduling DISASSEMBLY REPROCESSING META-HEURISTIC Q-LEARNING 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]