Improved Decentral Task Allocation for Autonomous Guided Vehicle Systems based on Karis Pro

Abstract

In this paper, we extended an existing decentralised method for allocating tasks to AGVs, by additionally considering vehicles which already are assigned to a task. This was achieved by also taking into account the opportunity costs arising from a vehicle passing a current task to another vehicle and subsequently accepting a new task. This loosened restriction is enabling the vehicle fleet for a higher flexibility, which can be used for improving the efficiency of the overall system. By means of simulation, our findings confirm the notion that our extended method - namely Karis Pro+ - leads to lower traffic density and higher flexibility, both of which are important KPI for large-scale transport vehicle systems.