Solving the AGV problem via a self-organizing neural network

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalNot applicablepeer-review

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Author(s)

Detail(s)

Original languageEnglish
Pages (from-to)1477-1493
Journal / PublicationJournal of the Operational Research Society
Volume47
Issue number12
StatePublished - Dec 1996

Abstract

Automated Guided Vehicle (AGV)-based material handling systems (MHS) are used widely in Flexible Manufacturing Systems (FMS). The problem of AGV consists of the decisions and the operational control strategies of dispatching, routeing and scheduling of a set of AGVs under given system environments and operational objectives. One remaining challenge is to develop effective methods of AGV decisions for improved system productivity. This paper describes a prototype neural network approach for the AGV problem in an FMS environment. A group of neural network models are proposed to perform dispatching and routeing tasks for the AGV under conditions of single or multiple vehicles, and with or without time windows. The goal is to satisfy the transport requests in the shortest time and in a non-conflicting manner, subject to the global manufacturing objectives. Based on Kohonen's self-organizing feature maps, we have developed efficient algorithms for the AGVs decisions, and simulation results have been very encouraging.

Research Area(s)

  • Automated guided vehicles, Flexible manufacturing system, Neural networks, Self-organizing feature maps

Citation Format(s)

Solving the AGV problem via a self-organizing neural network. / Hao, G.; Lai, K. K.

In: Journal of the Operational Research Society, Vol. 47, No. 12, 12.1996, p. 1477-1493.

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalNot applicablepeer-review