Research on Flexible Job Shop Scheduling Optimization Based on Segmented AGV

As a typical transportation tool in the intelligent manufacturing system, Automatic Guided Vehicle (AGV) plays an indispensable role in the automatic production process of the workshop. Therefore, integrating AGV resources into production scheduling has become a research hotspot. For the scheduling problem of the flexible job shop adopting segmented AGV, a dual-resource scheduling optimization mathematical model of machine tools and AGVs is established by minimizing the maximum completion time as the objective function, and an improved genetic algorithm is designed to solve the problem in this study. The algorithm designs a two-layer coding method based on process coding and machine tool coding and embeds the task allocation of AGV into the decoding process to realize the real dual resource integrated scheduling. When initializing the population, three strategies are designed to ensure the diversity of the population. In order to improve the local search ability and the quality of the solution of the genetic algorithm, three neighborhood structures are designed for variable neighborhood search. The superiority of the improved genetic algorithm and the influence of the location and number of transfer stations on scheduling results are verified in two cases.