Implementing graph coloring heuristic in construction phase of curriculum-based course timetabling problem

Curriculum-based course timetabling problem (CB-CTP) is a classical problem that is still being researched on nowadays. There are two phases in solving CB-CTP. The first phase is the construction phase where a population of initial solutions is generated. The second phase is the optimization phase w...

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Published in:IEEE Symposium on Computers and Informatics, ISCI 2013
Main Author: Azlan A.; Hussin N.M.
Format: Conference paper
Language:English
Published: IEEE Computer Society 2013
Online Access:https://www.scopus.com/inward/record.uri?eid=2-s2.0-84886498828&doi=10.1109%2fISCI.2013.6612369&partnerID=40&md5=c9052e9efaa914d792a5e96a6e3d91e1
id 2-s2.0-84886498828
spelling 2-s2.0-84886498828
Azlan A.; Hussin N.M.
Implementing graph coloring heuristic in construction phase of curriculum-based course timetabling problem
2013
IEEE Symposium on Computers and Informatics, ISCI 2013


10.1109/ISCI.2013.6612369
https://www.scopus.com/inward/record.uri?eid=2-s2.0-84886498828&doi=10.1109%2fISCI.2013.6612369&partnerID=40&md5=c9052e9efaa914d792a5e96a6e3d91e1
Curriculum-based course timetabling problem (CB-CTP) is a classical problem that is still being researched on nowadays. There are two phases in solving CB-CTP. The first phase is the construction phase where a population of initial solutions is generated. The second phase is the optimization phase where the final optimal solution is produced. While most papers concentrate on the optimization phase, this paper focuses on the construction phase due to the importance of initial solutions to the optimization phase. The initial solutions are generated by implementing graph coloring heuristic to the CB-CTP. The graph coloring heuristics used in this paper are largest degree and largest weighted degree. Result from computational experiment shows that both methods are able to produce population of initial solutions for almost every data instance. © 2013 IEEE.
IEEE Computer Society

English
Conference paper

author Azlan A.; Hussin N.M.
spellingShingle Azlan A.; Hussin N.M.
Implementing graph coloring heuristic in construction phase of curriculum-based course timetabling problem
author_facet Azlan A.; Hussin N.M.
author_sort Azlan A.; Hussin N.M.
title Implementing graph coloring heuristic in construction phase of curriculum-based course timetabling problem
title_short Implementing graph coloring heuristic in construction phase of curriculum-based course timetabling problem
title_full Implementing graph coloring heuristic in construction phase of curriculum-based course timetabling problem
title_fullStr Implementing graph coloring heuristic in construction phase of curriculum-based course timetabling problem
title_full_unstemmed Implementing graph coloring heuristic in construction phase of curriculum-based course timetabling problem
title_sort Implementing graph coloring heuristic in construction phase of curriculum-based course timetabling problem
publishDate 2013
container_title IEEE Symposium on Computers and Informatics, ISCI 2013
container_volume
container_issue
doi_str_mv 10.1109/ISCI.2013.6612369
url https://www.scopus.com/inward/record.uri?eid=2-s2.0-84886498828&doi=10.1109%2fISCI.2013.6612369&partnerID=40&md5=c9052e9efaa914d792a5e96a6e3d91e1
description Curriculum-based course timetabling problem (CB-CTP) is a classical problem that is still being researched on nowadays. There are two phases in solving CB-CTP. The first phase is the construction phase where a population of initial solutions is generated. The second phase is the optimization phase where the final optimal solution is produced. While most papers concentrate on the optimization phase, this paper focuses on the construction phase due to the importance of initial solutions to the optimization phase. The initial solutions are generated by implementing graph coloring heuristic to the CB-CTP. The graph coloring heuristics used in this paper are largest degree and largest weighted degree. Result from computational experiment shows that both methods are able to produce population of initial solutions for almost every data instance. © 2013 IEEE.
publisher IEEE Computer Society
issn
language English
format Conference paper
accesstype
record_format scopus
collection Scopus
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