Maintenance modelling of complex automated guided vehicle systems

Abstract

Automated guided vehicles (AGV’s) have been adopted in many industrial applications since their introduction in the 1950’s. Although still primarily used for the movement of materials around manufacturing facilities and warehouses they are also used in such applications as hospitals and transportation. Such driverless vehicles generally travel along a predefined route performing set tasks and they have been widely adopted due to their efficiency and economic benefits, Le-Anh and De Koster (2006). The availability of the vehicles is crucial to ensure that these benefits are maintained. As the complexity of industrial processes increases and fleets of AGV’s are commonly employed, maintenance and reliability issues are of increasing concern. In order to ensure that the benefits of AGV’s are utilised efficiently it is crucial that efficient maintenance strategies are employed. Hence in this work research has been undertaken into determining the optimal maintenance strategy for a complex multi AGV system. Typically a multi AGV system will consist of a number of vehicles that travel along the same route performing required tasks. Once any AGV fails it should be removed from the route as quickly as possible in order to prevent obstructing other AGV’s. In this work Coloured Petri Nets (CPN) and Genetic Algorithms are used in combination in order to determine the optimal maintenance strategy. From the research conducted it is found that the maintenance strategies adopted and the location of the maintenance site are significant factors impacting on the efficiency, cost, and productivity of a multi-AGV system