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Author: Andrew Greasley

Affiliation: Operations and Information Management Department, Aston University, Aston Triangle, Birmingham, U.K.

Keyword(s): Discrete-Event Simulation, Software Architectures, Machine Learning, Reinforcement Learning.

Abstract: A significant barrier to the combined use of simulation and machine learning (ML) is that practitioners in each area have differing backgrounds and use different tools. From a review of the literature this study presents five options for software architectures that combine simulation and machine learning. These architectures employ configurations of both simulation software and machine learning software and thus require skillsets in both areas. In order to further facilitate the combined use of these approaches this article presents a sixth option for a software architecture that uses a commercial off-the-shelf (COTS) DES software to implement both the simulation and machine learning algorithms. A study is presented of this approach that incorporates the use of a type of ML termed reinforcement learning (RL) which in this example determines an approximate best route for a robot in a factory moving from one physical location to another whilst avoiding fixed barriers. The study shows t hat the use of an object approach to modelling of the COTS DES Simio enables an ML capability to be embedded within the DES without the use of a programming language or specialist ML software. (More)

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Paper citation in several formats:
Greasley, A. (2020). Architectures for Combining Discrete-event Simulation and Machine Learning. In Proceedings of the 10th International Conference on Simulation and Modeling Methodologies, Technologies and Applications - SIMULTECH; ISBN 978-989-758-444-2; ISSN 2184-2841, SciTePress, pages 47-58. DOI: 10.5220/0009767600470058

@conference{simultech20,
author={Andrew Greasley.},
title={Architectures for Combining Discrete-event Simulation and Machine Learning},
booktitle={Proceedings of the 10th International Conference on Simulation and Modeling Methodologies, Technologies and Applications - SIMULTECH},
year={2020},
pages={47-58},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0009767600470058},
isbn={978-989-758-444-2},
issn={2184-2841},
}

TY - CONF

JO - Proceedings of the 10th International Conference on Simulation and Modeling Methodologies, Technologies and Applications - SIMULTECH
TI - Architectures for Combining Discrete-event Simulation and Machine Learning
SN - 978-989-758-444-2
IS - 2184-2841
AU - Greasley, A.
PY - 2020
SP - 47
EP - 58
DO - 10.5220/0009767600470058
PB - SciTePress

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