Flexible Job Shop Scheduling Optimization with Machine and AGV Integration Based on Improved NSGA-II

Abstract

Aiming at the problem of integrated scheduling of machines and AGVs in a flexible job shop, this paper constructs a scheduling model with the optimization objectives of minimizing the maximum completion time, minimizing the machine load, and minimizing the total energy consumption. This model is based on a comprehensive consideration of the payload time and no-load time of AGVs between the loading and unloading stations and the machining machines. An improved NSGA-II algorithm is proposed to address this problem. The algorithm adopts a three-level coding structure based on processes, machines, and AGVs, and employs differentiated cross-variation strategies for different levels to enhance its global search capability. A variable domain search algorithm is introduced to boost the local search capability by combining different neighborhood search methods within the three-level coding structure. Additionally, reverse individuals are introduced to improve the elite retention strategy, thereby increasing the diversity of the population. Ultimately, the case test results demonstrate that the improved NSGA-II algorithm exhibits superior performance in solving the flexible job shop scheduling problem involving AGVs, and the effect of the number of AGVs on the scheduling objectives conforms to the law of diminishing marginal utility.