Research on joint scheduling of AGVs and machines in multi-objective flexible job shop based on ISSA

Purpose Automated material handling, as the most important link in modern intelligent factory logistics, greatly determines the development of enterprises. The purpose of this study is to solve the cooperative scheduling problem of flexible workshop production process and automated guided vehicles (AGV) transportation (flexible jobshop scheduling problem-AGV) in an intelligent manufacturing system and get the scheduling scheme with the maximum job completion time and the shortest AGV transportation time. Design/methodology/approach The mathematical model of multi-objective optimization is established, and an improved sparrow method is proposed to solve the multi-objective optimization problem. First, to improve the diversity of the initial population, tent chaos mapping was used. Second, the resource manager location update formula is improved to improve the solving performance. Third, according to the iteration situation, an adaptive parameter adjustment strategy is proposed to adjust the safety threshold, explorer ratio and defender ratio. Finally, considering the characteristics of AGV and machine joint scheduling problem, a three-layer coding method is adopted, and cross-mutation operation of genetic algorithm is used to solve the problem. Findings The experimental findings show that the stability and optimization-seeking ability upgraded sparrow algorithm outperform traditional sparrow algorithm, genetic algorithm and genetic algorithm-based algorithm and coyote algorithm, and at the same time, the effect of the number of AGVs on the scheduling results is discovered. Originality/value This study proposes an improved sparrow algorithm, which has achieved good results in benchmark cases and literature examples and can be applied to other engineering scenarios to provide guidance for enterprise production.