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

Fuquan Wang, Yaping Fu (Corresponding author), Kaizhou Gao, Yaoxin Wu, Song Gao

Research output: Contribution to journalArticleAcademicpeer-review

7 Citations (Scopus)
5 Downloads (Pure)

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.

Original languageEnglish
Pages (from-to)184-209
Number of pages26
JournalComplex System Modeling and Simulation
Volume4
Issue number2
DOIs
Publication statusPublished - Jun 2024

Bibliographical note

Publisher Copyright:
© The author(s) 2024.

Keywords

  • disassembly
  • meta-heuristic
  • Q-learning
  • remanufacturing scheduling
  • reprocessing

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