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An Application of Genetic Algorithm for University Course Timetabling Problem

Sanjay R. Sutar, Rajan S. Bichkar. Published in Algorithms

International Journal of Applied Information Systems
Year of Publication: 2016
Publisher: Foundation of Computer Science (FCS), NY, USA
Authors: Sanjay R. Sutar, Rajan S. Bichkar
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  1. Sanjay R Sutar and Rajan S Bichkar. An Application of Genetic Algorithm for University Course Timetabling Problem. International Journal of Applied Information Systems 11(3):26-30, August 2016. URL, DOI BibTeX

    	author = "Sanjay R. Sutar and Rajan S. Bichkar",
    	title = "An Application of Genetic Algorithm for University Course Timetabling Problem",
    	journal = "International Journal of Applied Information Systems",
    	issue_date = "August 2016",
    	volume = 11,
    	number = 3,
    	month = "Aug",
    	year = 2016,
    	issn = "2249-0868",
    	pages = "26-30",
    	numpages = 5,
    	url = "",
    	doi = "10.5120/ijais2016451590",
    	publisher = "Foundation of Computer Science (FCS), NY, USA",
    	address = "New York, USA"


Timetabling problem is a process of assigning given set of events and resources to the limited space and time under hard constraints which are rigidly enforced and soft constraints which are satisfied as nearly as possible. As a kind of timetabling problems, University course timetabling is a very important administrative activity for a wide variety of institutes. Genetic algorithm is an advanced heuristics method which is very effective in many areas. It is frequently deployed meta-heuristics algorithm to solve difficult combinatorial optimization problems. In this paper, genetic algorithm is used to solve university course timetabling problem. At first, a model of problem to be solved is defined. Then, the genetic representation is determined and a fitness function is established according to the constraints. Finally, a case of university course timetabling from real-world is discussed and solved. It is demonstrated that the method proposed in this paper is feasible and efficient.


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Timetabling problems, Genetic algorithm, Optimization, Heuristic method