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Hybrid Genetic Algorithm for Solving Job-Shop Scheduling Problem
Melbourne, Australia July 11-July 13
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/ICIS.2007.1076th IEEE/ACIS International Conferenc ...
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S. M. Kamrul Hasan, Ruhul Sarker, David Cornforth, "Hybrid Genetic Algorithm for Solving Job-Shop Scheduling Problem," Computer and Information Science, ACIS International Conference on, pp. 519-524, 6th IEEE/ACIS International Conference on Computer and Information Science (ICIS 2007), 2007.
 
 
BibTex
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@article{ 10.1109/ICIS.2007.107,
author = {S. M. Kamrul Hasan and Ruhul Sarker and David Cornforth},
title = {Hybrid Genetic Algorithm for Solving Job-Shop Scheduling Problem},
journal ={Computer and Information Science, ACIS International Conference on},
volume = {0},
year = {2007},
isbn = {0-7695-2841-4},
pages = {519-524},
doi = {http://doi.ieeecomputersociety.org/10.1109/ICIS.2007.107},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
x 
 
TY - CONF
JO - Computer and Information Science, ACIS International Conference on
TI - Hybrid Genetic Algorithm for Solving Job-Shop Scheduling Problem
SN - 0-7695-2841-4
SP519
EP524
A1 - S. M. Kamrul Hasan,
A1 - Ruhul Sarker,
A1 - David Cornforth,
PY - 2007
KW - Hybrid Genetic Algorithm
KW - Job Pair Relationbased Representation
KW - Heuristic Ordering
KW - Job-Shop Scheduling.
VL - 0
JA - Computer and Information Science, ACIS International Conference on
ER -
 
S. M. Kamrul Hasan, Student Member, IEEE; University of New South Wales, Australia
Ruhul Sarker, Member, IEEE; University of New South Wales, Australia
David Cornforth, University of New South Wales, Australia
The Job-Shop Scheduling Problem (JSSP) is a well-known difficult combinatorial optimization problem. Many algorithms have been proposed for solving JSSP in the last few decades, including algorithms based on evolutionary techniques. However, there is room for improvement in solving medium to large scale problems effectively. In this paper, we present a Hybrid Genetic Algorithm (HGA) that includes a heuristic job ordering with a Genetic Algorithm. We apply HGA to a number of benchmark problems. It is found that the algorithm is able to improve the solution obtained by traditional genetic algorithm.
Index Terms:
Hybrid Genetic Algorithm, Job Pair Relationbased Representation, Heuristic Ordering, Job-Shop Scheduling.
Citation:
S. M. Kamrul Hasan, Ruhul Sarker, David Cornforth, "Hybrid Genetic Algorithm for Solving Job-Shop Scheduling Problem," icis, pp.519-524, 6th IEEE/ACIS International Conference on Computer and Information Science (ICIS 2007), 2007
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