Smíšené celočíselné programování vs. genetický algoritmus při rozvrhování proudové výroby

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26210%2F05%3APU53769" target="_blank" >RIV/00216305:26210/05:PU53769 - isvavai.cz</a>

Výsledek na webu

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DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Mixed Integer Programming vs. Genetic Algorithm Approach to Scheduling Permutation Flow Shop

Popis výsledku v původním jazyce

Flow shop scheduling problems represent scheduling a set of jobs (composed of tasks) in shops with a product machine layout. Thus, the jobs have the same manufacturing order. A permutation flow shop scheduling problem (PFSSP) is a special version of theproblem where each machine processes the jobs in the same order. In this paper, two different approaches to PFSSP with makespan objective are investigated. First a mixed integer programming model is formulated and it is used for solving the problem by ann optimisation package GAMS. Since the problem belongs to NP-complete problems, this approach is limited to smaller instances. Its reasonable bounds are indicated using benchmarks from OR-Library. For large instances, an approach using genetic algorithmis proposed including its appropriate parameter settings. Computational results show a good performance of genetic algorithm. For suitable parameter settings presented in the paper, this approach is able to find the optimal solution almos

Název v anglickém jazyce

Mixed Integer Programming vs. Genetic Algorithm Approach to Scheduling Permutation Flow Shop

Popis výsledku anglicky

Flow shop scheduling problems represent scheduling a set of jobs (composed of tasks) in shops with a product machine layout. Thus, the jobs have the same manufacturing order. A permutation flow shop scheduling problem (PFSSP) is a special version of theproblem where each machine processes the jobs in the same order. In this paper, two different approaches to PFSSP with makespan objective are investigated. First a mixed integer programming model is formulated and it is used for solving the problem by ann optimisation package GAMS. Since the problem belongs to NP-complete problems, this approach is limited to smaller instances. Its reasonable bounds are indicated using benchmarks from OR-Library. For large instances, an approach using genetic algorithmis proposed including its appropriate parameter settings. Computational results show a good performance of genetic algorithm. For suitable parameter settings presented in the paper, this approach is able to find the optimal solution almos

Druh

C - Kapitola v odborné knize

CEP obor

BB - Aplikovaná statistika, operační výzkum

OECD FORD obor

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Projekt

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Návaznosti

Z - Vyzkumny zamer (s odkazem do CEZ)

Rok uplatnění

2005

Kód důvěrnosti údajů

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

Název knihy nebo sborníku

Katalinic, B. (ed.): DAAAM International Scientific Book 2005

ISBN

3-901509-43-7

Počet stran výsledku

12

Strana od-do

579-590

Počet stran knihy

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Název nakladatele

DAAAM International

Místo vydání

Wien (Austria)

Kód UT WoS kapitoly

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