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Reseach Article

Integrating Genetic Algorithm, Tabu Search and Simulated Annealing For Job Shop Scheduling Proble

by R. Thamilselvan, P. Balasubramanie
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 48 - Number 5
Year of Publication: 2012
Authors: R. Thamilselvan, P. Balasubramanie
10.5120/7348-0283

R. Thamilselvan, P. Balasubramanie . Integrating Genetic Algorithm, Tabu Search and Simulated Annealing For Job Shop Scheduling Proble. International Journal of Computer Applications. 48, 5 ( June 2012), 42-54. DOI=10.5120/7348-0283

@article{ 10.5120/7348-0283,
author = { R. Thamilselvan, P. Balasubramanie },
title = { Integrating Genetic Algorithm, Tabu Search and Simulated Annealing For Job Shop Scheduling Proble },
journal = { International Journal of Computer Applications },
issue_date = { June 2012 },
volume = { 48 },
number = { 5 },
month = { June },
year = { 2012 },
issn = { 0975-8887 },
pages = { 42-54 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume48/number5/7348-0283/ },
doi = { 10.5120/7348-0283 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:43:20.945815+05:30
%A R. Thamilselvan
%A P. Balasubramanie
%T Integrating Genetic Algorithm, Tabu Search and Simulated Annealing For Job Shop Scheduling Proble
%J International Journal of Computer Applications
%@ 0975-8887
%V 48
%N 5
%P 42-54
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Job Shop Scheduling Problem (JSSP) is an optimization problem in which ideal jobs are assigned to resources at particular times. In recent years many attempts have been made at the solution of JSSP using a various range of tools and techniques such as Branch and Bound and Heuristics algorithms. This paper proposed a new algorithm based on Genetic Algorithm (GA), Tabu Search (TS) and Simulated Annealing (SA) algorithms to solve JSSP. The proposed algorithm is mainly based on the genetic algorithm. The reproduction phase of the genetic algorithm uses the tabu search to generate new population. Simulated annealing algorithm is used to speed up the genetic algorithm to get the solution by applying the simulated annealing test for all the population members. The proposed algorithm used many small but important features such as chromosome representation, effective genetic operators, and restricted neighbourhood strategies. The above features are used in the hybrid algorithm to solve several bench mark problems.

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Index Terms

Computer Science
Information Sciences

Keywords

Simulated Annealing Tabu Search Genetic Algorithm And Job Shop Scheduling