A random key-based genetic algorithm for AGV dispatching in FMS

Abstract

Automated Guided Vehicle (AGV) is a mobile robot used highly in industrial applications to move materials from point to point. AGV helps to reduce cost of manufacturing and increases efficiency in a manufacturing system. In this paper, we focus on the dispatching of AGVs in a Flexible Manufacturing System (FMS). A FMS environment requires a flexible and adaptable material handling system. To overcome the complex system constraints of AGV dispatching in FMS, we model an AGV system by using network structure. We also propose an effective evolutionary approach for solving this problem as a network optimisation problem by Random Key-based Genetic Algorithm (RKGA). The objective is minimising the time required to complete all jobs (i.e. makespan). Numerical experiments for case study show the effectiveness of the proposed approach.