A neural network model for the free-ranging AGV route-planning problem View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

1996-06

AUTHORS

Gang Hao, Jen S. Shang, Luis G. Vargas

ABSTRACT

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. More... »

PAGES

217-227

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/bf00118081

DOI

http://dx.doi.org/10.1007/bf00118081

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1009350149


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