Using Adaptive Neural Networks for Optimising Discrete Event Simulation

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F49777513%3A23210%2F24%3A43972390" target="_blank" >RIV/49777513:23210/24:43972390 - isvavai.cz</a>

Alternative codes found

RIV/49777513:23420/24:43972390 RIV/49777513:23520/24:43972390

Result on the web

<a href="https://www.ijsimm.com/Full_Papers/Fulltext2024/text23-2_678.pdf" target="_blank" >https://www.ijsimm.com/Full_Papers/Fulltext2024/text23-2_678.pdf</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.2507/IJSIMM23-2-678" target="_blank" >10.2507/IJSIMM23-2-678</a>

Result language

angličtina

Original language name

Using Adaptive Neural Networks for Optimising Discrete Event Simulation

Original language description

The paper presents the use of adaptive neural networks for carrying out simulation optimisation using digital models (discrete event simulation models) created in accordance with the Industry 4.0 concept. The digital models reflect different problems in industrial engineering. The simulation optimisers use an adaptive neural network to find the best settings of the digital models according to defined objective functions for each model. We compared the effectiveness (using different evaluation criteria) of the adaptive neural network (ANN) optimisation method used on 6 different discrete event simulation models. We compared adaptive neural networks with 11 optimisation methods - pseudo gradient, metaheuristic, evolutionary and swarm optimisation methods (and their combinations). The ANN method demonstrated the ability to efficiently find the global optimum of the objective function in different cases of the objective function - the ANN method is in the top 5 best tested methods from the 12 optimisation methods.

Czech name

—

Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

—

OECD FORD branch

21100 - Other engineering and technologies

Project

—

Continuities

S - Specificky vyzkum na vysokych skolach

Publication year

2024

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

International Journal of Simulation Modelling

ISSN

1726-4529

e-ISSN

1996-8566

Volume of the periodical

23

Issue of the periodical within the volume

2

Country of publishing house

AT - AUSTRIA

Number of pages

12

Pages from-to

227-238

UT code for WoS article

001244991400003

EID of the result in the Scopus database

2-s2.0-85197863316