An effective and robust flow path network is desired in Automated Guided Vehicle (AGV) systems. A design process to obtain the desired flow path network in AGV systems is proposed in this paper. Our proposed method can make flow path networks robust against tasks, which include pick-up point, drop-off point and throughput and number of AGVs . It is important for this robust flow path network that the kinds of tasks be of various and non-linear to the system effectiveness. The problem is solved by the design method of various kinds of tasks that are difficult for AGV systems using Genetic Algorithm (GA). An effective flow path network is designed with GA simultaneously because the difficult tasks and number of AGVs depend on the flow path networks. Competitive coevolution is applied to the simultaneous design. AGV systems can be effective with uni/bi-directional combined flow path networks which utilize just simple routings. Results of the design are shown through simulations, and the designed flow path network makes it possible to complete various tasks with various numbers of AGVs.
Scheduling of Automated Guided Vehicle and Flexible Jobshop using Jumping Genes Genetic Algorithm