@article{Wang2024, 
author = {Fuquan Wang and Yaping Fu and Kaizhou Gao and Yaoxin Wu and Song Gao},
title = {A Q-Learning Based Hybrid Meta-Heuristic for Integrated Scheduling of Disassembly and Reprocessing Processes Considering Product Structures and Stochasticity},
year = {2024},
journal = {Complex System Modeling and Simulation},
volume = {4},
number = {2},
pages = {184-209},
keywords = {remanufacturing scheduling, disassembly, reprocessing, meta-heuristic, Q-learning},
url = {https://www.sciopen.com/article/10.23919/CSMS.2024.0007},
doi = {10.23919/CSMS.2024.0007},
abstract = {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.}
}