108 / 2023-09-19 20:17:57
A hybrid particle swarm optimization based on Q-learning for multiobjective distributed flow-shop scheduling problem
distributed flow-shop scheduling problem,particle swarm algorithm,Q-learning,variable neighborhood search
Final Paper
Chen Li / Dalian University of Technology
Wenqiang Zhang / Henan University of Technology
Lin Lin / Dalian University of Technology
This study addresses the distributed flow-shop scheduling problem (DFSP) and aims to minimize the makespan and the total processing time. Although many intelligent algorithms have been proposed to solve DFSP, the efficiency and quality of these solutions still need further improvement to meet higher production requirements. Therefore, a hybrid particle swarm optimization with enhanced directional search and Q-learning-based variable neighborhood search is proposed. The directional search quickly explores the particle swarm in multiple directions, which enhances the convergence ability in different areas of Pareto front. The Q-learning-based variable neighborhood local search prevents the proposed algorithm from falling into a local optimum. The comparative results and statistical analysis of the experiments demonstrate the superior convergence and distribution performance of the proposed algorithm.
Important Date
  • Conference Date

    Nov 02

    2023

    to

    Nov 04

    2023

  • Dec 15 2023

    Draft paper submission deadline

  • Dec 20 2023

    Registration deadline

Sponsored By
IEEE Instrumentation and Measurement Society
Xidian University
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