Integrated Scheduling Method for Flexible Square Part Processing Machines and AGVs based on the Two-Stage GP Algorithm

With the accelerated transition towards intelligent manufacturing, research on the integrated scheduling of machines and AGVs in a flexible square parts processing workshop is essential. This paper addresses this NP-hard problem by proposing an integrated scheduling method for flexible square parts processing machines and AGVs based on a two-stage GP algorithm. Initially, a three-stage sampling process for square plates is completed using the FFD algorithm and a common strip approximation algorithm, optimizing plate utilization, loss area, and usage. Subsequently, a mathematical model is established with the objective function of minimizing the maximum completion time, considering the actual working conditions of processing machines and AGVs. The model is divided into discrete scheduling and online scheduling modules, and high-quality combined scheduling rules are mined using the two-stage GP algorithm and tested in a virtual simulation environment. Ablation experiments reveal that the mined high-quality combined scheduling rules outperform traditional combination rules in reducing the maximum completion time and the total AGV working path. Specifically, the rules derived from the GP algorithm show significant optimization improvements over traditional rules in terms of maximum completion time and total AGV working path. Furthermore, experiments indicate that increasing the number of AGVs reduces the overall completion time and total AGV working path, although the reduction rate diminishes as the number of AGVs increases. Properly setting processing times can enhance workshop operational efficiency, and it is recommended that manufacturing enterprises optimize processing times based on their actual production mode to improve machine utilization and reduce order waiting times.