Tutorial: Artificial Neural Networks for Discrete-Event Simulation

This advanced tutorial explores some recent applications of artificial neural networks (ANNs) to stochastic discrete-event simulation (DES). We first review some basic concepts and then give examples of how ANNs are being used in the context of DES to facilitate simulation input modeling, random variate generation, simulation metamodeling, optimization via simulation, and more. Combining ANNs and DES allows exploitation of the deep domain knowledge embodied in simulation models while simultaneously leveraging the ability of modern ML techniques to capture complex patterns and relationships in data.

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Publication Details

Published
2024-12-15
DOI
https://doi.org/10.1109/wsc63780.2024.10838940
Citations
4
Primary Topic
Simulation Techniques and Applications
Type
article
Field-Weighted Citation Impact
1.96
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article

Tutorial: Artificial Neural Networks for Discrete-Event Simulation

Peter J. Haas
4 citations
Simulation Techniques and Applications
1.96
article

Tutorial: Artificial Neural Networks for Discrete-Event Simulation

Peter J. Haas
article en
4 citations

Abstract

This advanced tutorial explores some recent applications of artificial neural networks (ANNs) to stochastic discrete-event simulation (DES). We first review some basic concepts and then give examples of how ANNs are being used in the context of DES to facilitate simulation input modeling, random variate generation, simulation metamodeling, optimization via simulation, and more. Combining ANNs and DES allows exploitation of the deep domain knowledge embodied in simulation models while simultaneously leveraging the ability of modern ML techniques to capture complex patterns and relationships in data.

Amherst College (US)
Openalex Percentile: Top 12%
Simulation Techniques and Applications
1.96
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