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Genetic Algorithm Tuning Applied to the Open Shop Scheduling Problem

by Chaouqi Mohsine, Benhra Jamal, My Ali El Oualidi
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International Journal of Applied Information Systems
Foundation of Computer Science (FCS), NY, USA
Volume 11 - Number 3
Year of Publication: 2016
Authors: Chaouqi Mohsine, Benhra Jamal, My Ali El Oualidi
10.5120/ijais2016451588

Chaouqi Mohsine, Benhra Jamal, My Ali El Oualidi . Genetic Algorithm Tuning Applied to the Open Shop Scheduling Problem. International Journal of Applied Information Systems. 11, 3 ( Aug 2016), 21-25. DOI=10.5120/ijais2016451588

@article{ 10.5120/ijais2016451588,
author = { Chaouqi Mohsine, Benhra Jamal, My Ali El Oualidi },
title = { Genetic Algorithm Tuning Applied to the Open Shop Scheduling Problem },
journal = { International Journal of Applied Information Systems },
issue_date = { Aug 2016 },
volume = { 11 },
number = { 3 },
month = { Aug },
year = { 2016 },
issn = { 2249-0868 },
pages = { 21-25 },
numpages = {9},
url = { https://www.ijais.org/archives/volume11/number3/926-2016451588/ },
doi = { 10.5120/ijais2016451588 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2023-07-05T19:03:55.938427+05:30
%A Chaouqi Mohsine
%A Benhra Jamal
%A My Ali El Oualidi
%T Genetic Algorithm Tuning Applied to the Open Shop Scheduling Problem
%J International Journal of Applied Information Systems
%@ 2249-0868
%V 11
%N 3
%P 21-25
%D 2016
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The present paper deals with the open-shop scheduling problem using a manual tuning of a genetic algorithm’s parameters. A comparison has been performed between Taillard’s Benchmarks for 60 instances, 2 dispatching rules and 198 variants from the GA algorithm obtained by changing the population size, the generation’s number, the crossover probability, and the mutation probability. Interesting results were obtained leading to some conclusions for the best choice of the parameters.

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Index Terms

Computer Science
Information Sciences

Keywords

Scheduling Open shop Genetic Algorithms Tuning Benchmarks

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