Improved Decentral Task Allocation for Autonomous Guided Vehicle Systems based on Karis Pro
Schlagworte:AGV, Karis Pro, Task Allocation, Simulation, AnyLogic
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.
Copyright (c) 2020 Maximilian Selmair
Dieses Werk steht unter der Lizenz Creative Commons Namensnennung - Nicht-kommerziell 4.0 International.