Using Adaptive Neural Networks for Optimising Discrete Event Simulation

Kód výsledku v 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>

Nalezeny alternativní kódy

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

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

Using Adaptive Neural Networks for Optimising Discrete Event Simulation

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Using Adaptive Neural Networks for Optimising Discrete Event Simulation

Popis výsledku anglicky

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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

21100 - Other engineering and technologies

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2024

Kód důvěrnosti údajů

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

Název periodika

International Journal of Simulation Modelling

ISSN

1726-4529

e-ISSN

1996-8566

Svazek periodika

23

Číslo periodika v rámci svazku

2

Stát vydavatele periodika

AT - Rakouská republika

Počet stran výsledku

12

Strana od-do

227-238

Kód UT WoS článku

001244991400003

EID výsledku v databázi Scopus

2-s2.0-85197863316