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 sched...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.展开更多
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)+2 种基金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).
文摘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.
