Deep reinforcement learning-based memetic algorithm for solving dynamic distributed green flexible job shop scheduling problem with finite transportation resources

Xinxin Zhou, Fuyu Wang, Bin Wu, Yan Li, Nannan Shen

Research output: Contribution to journalArticlepeer-review

Abstract

To solve the dynamic distributed green flexible job shop scheduling problem with integrated multi-automated guided vehicles (AGVs) transportation (DDGFJSP-MT), a coupled mathematical model is constructed in this study with the objective of minimizing the makespan and total carbon emissions. The complex coupled roles between factories, jobs, machines, and AGVs induced by machine breakdown are explored. Meanwhile, a deep Q-network-based dynamic efficient memetic algorithm (DQN-DEMA) is proposed to solve the problem. First, a four-layer coding is designed to characterize the DDGFJSP-MT, and a novel dynamic decoding technique is developed based on the state variations of the involved subjects and their strong coupling effects following the machine breakdown. Second, an alternating hybrid initialization strategy is employed to improve the quality and diversity of the rescheduled population. Then, several neighborhood search structures are designed based on critical path and bottleneck operation, and DQN is applied to recommend the most suitable local search operator for each elite individual, accelerating the convergence of the rescheduled population and effectively avoiding the waste of algorithmic resources. Finally, performance validation on 20 instances demonstrates that DQN-DEMA obtains the Pareto frontier with higher quality and diversity in 15 instances compared to the six state-of-the-art algorithms.

Original languageEnglish
Article number101885
JournalSwarm and Evolutionary Computation
Volume94
DOIs
StatePublished - Apr 2025

Keywords

  • Deep reinforcement learning
  • Distributed flexible job shop scheduling
  • Finite AGV transportation
  • Machine breakdown
  • Memetic algorithm

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