grasp and guided local search for the examination timetabling problem

Int. J. Artificial Intelligence and Soft Computing, Vol. 2, Nos. 1/2, 2010 103 Copyright © 2010 Inderscience Enterprises Ltd. Grasp and Guided Local Search for the examination timetabling problem Drifa Hadjidj* Faculté des Sciences, Département d’Informatique, Université M’hamed Bougara, Boumerdès, Avenue de l’indépendance 35000, Boumerdès, Algerie E-mail: [email protected] *Corresponding author Habiba Drias Faculté Génie Electrique et Informatique, Département d’Informatique, Université des Sciences et de la Technologie Houari Boumediene, BP 32, El-AAlia 16111, Babezzouar, Alger, Algerie E-mail: [email protected] Abstract: Examination timetabling is an optimisation problem, which regards the scheduling of a set of exams to a set of contiguous time slots, satisfying a set of constraints. The problem belongs to the class of NP-Complete problems and is usually tackled using heuristic methods. In this paper, we describe a solution algorithm and its implementation which makes use of the good features of a Greedy Randomised Adaptive Search Procedure (GRASP) and the Guided Local Search (GLS) meta-heuristic. The implementation of the algorithm has been experimented on the popular Carter’s benchmarks and compared with the best recent results. Keywords: examination timetabling; grasp; GLS; guided local search; meta-heuristic; graph heuristics. Reference to this paper should be made as follows: Hadjidj, D. and Drias, H. (2010) ‘Grasp and Guided Local Search for the examination timetabling problem’, Int. J. Artificial Intelligence and Soft Computing, Vol. 2, Nos. 1/2, pp.103–114. Biographical notes: Drifa Hadjidj is an Assistant Professor in the Department of Computer Science at UMBB (Boumerdès, Algeria). She received both her Engineering Degree (Ingénieur d’état) in Computer Science and her Magister Degree in Operational Research from USTHB in Algeria. Her primary areas of interest include artificial intelligence, evolutionary computation, meta-heuristics, operational research, graph drawing and computational complexity.

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Int. J. Artificial Intelligence and Soft Computing, Vol. 2, Nos. 1/2, 2010 103

Grasp and Guided Local Search for the examination timetabling problem

Drifa Hadjidj* Faculté des Sciences, Département d’Informatique, Université M’hamed Bougara, Boumerdès, Avenue de l’indépendance 35000, Boumerdès, Algerie E-mail: [email protected] *Corresponding author

Habiba Drias Faculté Génie Electrique et Informatique, Département d’Informatique, Université des Sciences et de la Technologie Houari Boumediene, BP 32, El-AAlia 16111, Babezzouar, Alger, Algerie E-mail: [email protected]

Abstract: Examination timetabling is an optimisation problem, which regards the scheduling of a set of exams to a set of contiguous time slots, satisfying a set of constraints. The problem belongs to the class of NP-Complete problems and is usually tackled using heuristic methods. In this paper, we describe a solution algorithm and its implementation which makes use of the good features of a Greedy Randomised Adaptive Search Procedure (GRASP) and the Guided Local Search (GLS) meta-heuristic. The implementation of the algorithm has been experimented on the popular Carter’s benchmarks and compared with the best recent results.

Keywords: examination timetabling; grasp; GLS; guided local search; meta-heuristic; graph heuristics.

Reference to this paper should be made as follows: Hadjidj, D. and Drias, H. (2010) ‘Grasp and Guided Local Search for the examination timetabling problem’, Int. J. Artificial Intelligence and Soft Computing, Vol. 2, Nos. 1/2, pp.103–114.

Biographical notes: Drifa Hadjidj is an Assistant Professor in the Department of Computer Science at UMBB (Boumerdès, Algeria). She received both her Engineering Degree (Ingénieur d’état) in Computer Science and her Magister Degree in Operational Research from USTHB in Algeria. Her primary areas of interest include artificial intelligence, evolutionary computation, meta-heuristics, operational research, graph drawing and computational complexity.

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