Abstract
To address the resources optimization problem of AGV-served manufacturing systems driven by multi-varieties and small-batch production orders, a scheduling model integrating machines and automated guided vehicles (AGVs) is proposed. In this model, the makespan of jobs from raw material storage to finished parts storage through multi-stage processes has been used as the objective function, and the utilization ratios of machines and AGVs have been used as the comprehensive evaluation functions. An improved particle swarm optimization algorithm with the characteristics of main particles and nested particles is developed to solve a reasonable scheduling scheme. Compared with the basic particle swarm optimization algorithm and genetic algorithm, the numerical result suggests that the nested particle swarm optimization algorithm has more advantages in convergence and solving efficiency. It is expected that this study can provide a useful reference for the flexible adjustment of AGV-served manufacturing systems.
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