Multi-objective optimization for periodic preventive maintenance

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27510%2F15%3A10240757" target="_blank" >RIV/61989100:27510/15:10240757 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1109/IESM.2015.7380154" target="_blank" >http://dx.doi.org/10.1109/IESM.2015.7380154</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/IESM.2015.7380154" target="_blank" >10.1109/IESM.2015.7380154</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Multi-objective optimization for periodic preventive maintenance

Popis výsledku v původním jazyce

This article investigates a JIT single machine scheduling problem with a periodic preventive maintenance. Also to maintain the quality of the products, there is a limitation on the maximum number of allowable jobs in each period. The proposed bi-objective mixed integer model minimizes total earliness-tardiness and makespan simultaneously. Due to the computational complexity of the problem, multi-objective particle swarm optimization (MOPSO) algorithm is implemented. Also, as well as MOPSO, two other optimization algorithms are used for comparing the results. Eventually, Taguchi method with metrics analysis is presented to tune the algorithms&apos; parameters and a multiple criterion decision making (MCDM) technique based on the technique for order of preference by similarity to ideal solution (TOPSIS) is applied to choose the best algorithm. Comparison results confirmed supremacy of MOPSO to the other algorithms. (C) 2015 International Institute for Innovation, Industrial Engineering and Entrepreneurship - I4e2.

Název v anglickém jazyce

Multi-objective optimization for periodic preventive maintenance

Popis výsledku anglicky

This article investigates a JIT single machine scheduling problem with a periodic preventive maintenance. Also to maintain the quality of the products, there is a limitation on the maximum number of allowable jobs in each period. The proposed bi-objective mixed integer model minimizes total earliness-tardiness and makespan simultaneously. Due to the computational complexity of the problem, multi-objective particle swarm optimization (MOPSO) algorithm is implemented. Also, as well as MOPSO, two other optimization algorithms are used for comparing the results. Eventually, Taguchi method with metrics analysis is presented to tune the algorithms&apos; parameters and a multiple criterion decision making (MCDM) technique based on the technique for order of preference by similarity to ideal solution (TOPSIS) is applied to choose the best algorithm. Comparison results confirmed supremacy of MOPSO to the other algorithms. (C) 2015 International Institute for Innovation, Industrial Engineering and Entrepreneurship - I4e2.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

50204 - Business and management

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2015

Kód důvěrnosti údajů

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

Název statě ve sborníku

Proceedings of 2015 International Conference on Industrial Engineering and Systems Management, IEEE IESM 2015

ISBN

978-2-9600532-6-5

ISSN

—

e-ISSN

neuvedeno

Počet stran výsledku

10

Strana od-do

173-182

Název nakladatele

Institute of Electrical and Electronics Engineers

Místo vydání

New York

Místo konání akce

Sevilla

Datum konání akce

21. 10. 2015

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000380454700021