Influence of ea control parameters to optimization process of fjssp problem

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F46747885%3A24210%2F20%3A00007861" target="_blank" >RIV/46747885:24210/20:00007861 - isvavai.cz</a>

Výsledek na webu

<a href="http://www.ijsimm.com/Full_Papers/Fulltext2020/text19-3_519.pdf" target="_blank" >http://www.ijsimm.com/Full_Papers/Fulltext2020/text19-3_519.pdf</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.2507/IJSIMM19-3-519" target="_blank" >10.2507/IJSIMM19-3-519</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Influence of ea control parameters to optimization process of fjssp problem

Popis výsledku v původním jazyce

The ability of Evolution Algorithms (EA) to find an optimal solution is usually given by various algorithm operators. Population size and a maximal number of generations are usually set base on available timespan. Setting selection and elimination methods together with crossover probability are usually based on intuition and sometimes are problem specific. That is the reason presented research is focusing on the approach of how to set elimination methods and crossover probability by the statistical approach to eliminate the necessity of experience and intuition. This article describes the scheduling model together with the used EA to solve the Flexible Job Shop Scheduling Problem (FJSSP). Statistical process control methods are applied as there is a designed experiment to find out the statistical significance of each parameter during solving one of the FJSSP hardest problems. Crossover and elimination statistical importance are analysed and suitable levels of them are suggested. The statistical approach as a possible methodology to set the mentioned parameters is then discussed. This paper was written with aid of students (František Manlig and Pandiyaraj Gnanasekar).

Název v anglickém jazyce

Influence of ea control parameters to optimization process of fjssp problem

Popis výsledku anglicky

The ability of Evolution Algorithms (EA) to find an optimal solution is usually given by various algorithm operators. Population size and a maximal number of generations are usually set base on available timespan. Setting selection and elimination methods together with crossover probability are usually based on intuition and sometimes are problem specific. That is the reason presented research is focusing on the approach of how to set elimination methods and crossover probability by the statistical approach to eliminate the necessity of experience and intuition. This article describes the scheduling model together with the used EA to solve the Flexible Job Shop Scheduling Problem (FJSSP). Statistical process control methods are applied as there is a designed experiment to find out the statistical significance of each parameter during solving one of the FJSSP hardest problems. Crossover and elimination statistical importance are analysed and suitable levels of them are suggested. The statistical approach as a possible methodology to set the mentioned parameters is then discussed. This paper was written with aid of students (František Manlig and Pandiyaraj Gnanasekar).

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

2020

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

—

Svazek periodika

19

Číslo periodika v rámci svazku

3

Stát vydavatele periodika

AT - Rakouská republika

Počet stran výsledku

12

Strana od-do

387-398

Kód UT WoS článku

000571403200003

EID výsledku v databázi Scopus

2-s2.0-85095845895