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An artificial neural network approach to discrete-event simulation

Published online by Cambridge University Press:  27 February 2009

Ian Flood  and
Kenneth Worley
Ian Flood
Affiliation:
Department of Civil Engineering, University of Maryland, College Park, MD 20742, USA
Kenneth Worley
Affiliation:
Department of Civil Engineering, University of Maryland, College Park, MD 20742, USA
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Abstract

This paper proposes and evaluates a neural network-based method for simulating manufacturing processes that exhibit both noncontinuous and stochastic behavior processes more conventionally modeled, using discrete-event simulation algorithms. The incentive for developing the technique is its potential for rapid execution of a simulation through parallel processing, and facilitation of the development and improvement of models particularly where there is limited theory describing the dependence between component processes. A brief introduction is provided to a radial-Gaussian neural network architecture and training process, the system adopted for the work presented in this paper. A description of the basic approach proposed for applying this technology to simulation is then described. This involves the use of a modularized neural network approach to model construction and the prediction of the occurrence of events using information retained from several previous states of the simulation. A class of earth-moving systems, comprising a push-dozer and a fleet of scrapers, is used as the basis for assessing the viability and performance of the proposed approach. A series of experiments show the neural network to be capable of both capturing the characteristic behavior and making an accurate prediction of production rates of scraper-based earth-moving systems. The paper concludes with an indication of some areas for further development and evaluation of the technique.

Keywords

Neural NetworksDiscrete-Event SimulationStochastic Modeling
Type
Articles
Information
AI EDAM , Volume 9 , Issue 1 , January 1995 , pp. 37 - 49
Copyright
Copyright © Cambridge University Press 1995

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