A Learning-Based Two-Stage Multi-Thread Iterated Greedy Algorithm for Co-Scheduling of Distributed Factories and Automated Guided Vehicles With Sequence-Dependent Setup Times

Automated guided vehicles are widely utilized in the real production environment for tasks such as job transfer and inter-factory collaboration, yet they remain relatively underexplored in academic research. This study addresses the distributed permutation flow shop co-scheduling problem with sequence-dependent setup times (DPFCSP-SDST). We propose a novel solution that leverages an optimization algorithm, specifically a learning-based two-stage multi-thread iterated greedy algorithm (LTMIG). First, a problem-specific initialization method is designed to generate the initialization solution in two stages. Second, a Q-learning-based operator adaptation strategy is adopted to guide the evolutionary direction of factory assignment to reduce the makespan. Then, the proposed destructive-construction strategy builds an archive set to share historical knowledge with different stages of search, ensuring exploration capability. Local search effectively combines the parallel computing power of multi-threading with the inherent exploitation capability of LTMIG, and fully utilizes the information of elite solutions. Extensive experimental results demonstrate that LTMIG is significantly better than the comparison algorithms mentioned in the paper, and it turns out that LTMIG is the most suitable algorithm for solving DPFCSP-SDST.