Adaptive AGV fleet management in a dynamically changing production environment

Abstract In the era of smart manufacturing, autonomous mobile robots have become affordable for numerous companies, although the fleet management remains a challenging problem. A novel approach is proposed, supporting the solution of vehicle assignment problem. The method relies on adaptive workstation clustering that considers not only complex environment layout, but also the main characteristics of the material flow. The technique combines network analytical and optimization tools with a greedy algorithm of refinement. The implementation is presented, and the impact of clustering techniques on selected performance metrics are analyzed within a series of experiments, taken from an industrial case study.