A value-added approach for automated guided vehicle task assignment

Abstract Efficient material flow is essential in achieving high productivity levels. Design and control of the material handling system directly affect the efficiency of the material flow. In the case of automated guided vehicle (AGV) systems, this translates to guidepath design, AGV dispatching, scheduling, and routing. The focus of this paper is on vehicle dispatching, or vehicle task assignment, which is defined as the selection of the next load for pickup and delivery. The algorithm developed in this research is inherently a demand-driven strategy (that is, pull), which further prioritizes the loads requiring an AGV according to the value added to them as they go through the manufacturing processes. Benchmarking for several performance factors, such as throughput and flow time, through simulation experiments shows this algorithm to outperform some of the best dispatching rules reported in the literature.