Abstract
The paper suggests a novel workflow to solving adaptively the automated guided vehicle (AGV) fleet management problem with the key objective to serve production flawlessly. Departing from a material flow network model it is shown how an analysis using recent notions and methods of network science can detect the hidden structure of the overall problem. Once uncovered, this modularity-based structuring is exploited when balancing the expected load of vehicles and dynamically deciding their final dispatching. The paper presents stages of the workflow and the merits of its application on a series of comparative simulation studies taken from industrial experience.
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