An integrated scheduling approach considering dispatching strategy and conflict-free route of AMRs in flexible job shop

To reveal the profound impact of dispatching strategy and route of autonomous mobile robot (AMR) on scheduling in the flexible job shop with AMRs, this study presents the bi-level programming model for integrated scheduling with machines and AMRs (ISMV), subdividing distributed shared dispatching strategy (DSDS) and following dispatching strategy (FDS) for AMRs. The integrated scheduling model is developed at the upper level with the objective of minimizing the makespan, and the AMR conflict-free route planning (CFRP) model is formulated at the lower level to minimize travel time. To solve the model, a novel algorithmic framework (SLGA-D) composed of the self-learning genetic algorithm (SLGA) and Dijkstra with time window (DijkstraTW) is designed. The SLGA is formed by embedding the Q-learning into genetic algorithm to intelligently adjust crossover probability and mutation probability. Several experiments are implemented to validate the SLGA-D and the model proposed in this study. The results of experiments prove that reinforcement learning mechanism within Q-learning successfully enhances the global search ability of the algorithm, and production scheduling and distribution route are optimized synergistically by the model. Sensitivity analyses reveal that, as the number of AMRs rises, the makespan tends to stabilize after decreasing rapidly to a threshold value. Once the number of AMRs exceeds a certain threshold (w=5\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$w = 5$$\end{document} in this study), it is not significant to shorten the makespan by continuing to invest AMRs. When the number of production tasks to be processed does not exceed the number of AMRs, following dispatching strategy of AMR is more favorable; conversely, distributed shared dispatching strategy is superior. The results provide guideline and inspiration for managers who are committed to manufacturing schedule and control.