This paper describes the development of a prototype neural network model for the free-ranging AGV route-planning problem. The vehicle planner operates in quasi-real time. A small planning horizon is set and all transport requests existing at the beginning of a planning horizon are examined. A neural network model is proposed to perform dispatching and routing tasks for the AGVs. Its goal is to satisfy the transport requests in the shortest time and in a non-conflicting manner, subject to the global manufacturing objective of maximizing throughputs. Based on Kohonen's self-organizing feature maps, we develop three efficient planning algorithms for the single and multiple AGV problems. The simulation results indicate that the proposed neural network approach gives very efficient solutions.
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Hao, G., Shang, J.S. & Vargas, L.G. A neural network model for the free-ranging AGV route-planning problem. J Intell Manuf 7, 217–227 (1996). https://doi.org/10.1007/BF00118081
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DOI: https://doi.org/10.1007/BF00118081