Artificial intelligence techniques in the scheduling and routing of automated guided vehicle systems
This dissertation examines the problems of scheduling and routing automated guided vehicles (AGVs). AGVs are unmanned vehicles following a network of guide paths controlled by a supervisory controller. They represent a highly flexible material moving system well suited to the developing technologies of advanced manufacturing. The research in this area is motivated by the importance of AGVs in modern factories and the inadequacies of current methods of control. These inadequacies include the lack of adequate temporal reasoning, production of vehicle schedules that support automated guided vehicle system (AGVS) rather than global manufacturing objectives, the inability to produce quickest routes for vehicles and the lack of dynamic replanning. Unlike conventional controllers of AGVs the AGV scheduler presented in this dissertation (PRISMM - Planner for Reactive Intelligent Scheduling of Material Movement) considers global manufacturing objectives as well as local AGVS objectives when generating AGV schedules. It exploits novel techniques for routing vehicles that uniquely allow the integration of the shortest or the quickest routes into a timetable of vehicle movements. The techniques guarantee to find a route if one exists and will allow the imposition of deadlines. Algorithms to find a fast route through large or extremely busy route networks have also been derived. These methods will guarantee to find a route between two points as long as the vehicle begins its journey from a reserved parking space. A replanning technique has been proposed for dealing with most common errors of AGV schedule execution. A new method for the most commonly encountered problem requires no reordering of plan steps.