Lower bounds for the ITC-2007 curriculum-based course timetabling problem

This paper describes an approach for generating lower bounds for the curriculum-based course timetabling problem, which was presented at the International Timetabling Competition (ITC-2007, Track 3). So far, several methods based on integer linear programming have been proposed for computing lower b...

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Veröffentlicht in:European journal of operational research Jg. 212; H. 3; S. 464 - 472
Hauptverfasser: Hao, Jin-Kao, Benlic, Una
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Amsterdam Elsevier B.V 01.08.2011
Elsevier
Elsevier Sequoia S.A
Schriftenreihe:European Journal of Operational Research
Schlagworte:
Applied sciences
Benchmarking
Bounds
Bounds Partitioning Tabu search Timetabling
Computation
Computer Science
Curricula
Curriculum development
Divide & conquer algorithms
Estimates
Exact sciences and technology
Heuristic
Integer programming
Linear programming
Lower bounds
Mathematical programming
Operational research
Operational research and scientific management
Operational research. Management science
Optimization
Optimization algorithms
Partitioning
Scheduling, sequencing
Solvers
Studies
Tabu search
Timetabling
Upper bounds
Timetabling
Partitioning
Tabu search
Bounds
Lower bound
Partition
Curriculum guides
Minimization
Iterative method
Linear programming
Integer programming
Educational program
Upper bound
Heuristic method
Timetabling problem
Divide and conquer method
ISSN:0377-2217, 1872-6860
Online-Zugang:Volltext
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Beschreibung
Zusammenfassung:This paper describes an approach for generating lower bounds for the curriculum-based course timetabling problem, which was presented at the International Timetabling Competition (ITC-2007, Track 3). So far, several methods based on integer linear programming have been proposed for computing lower bounds of this minimization problem. We present a new partition-based approach that is based on the “divide and conquer” principle. The proposed approach uses iterative tabu search to partition the initial problem into sub-problems which are solved with an ILP solver. Computational outcomes show that this approach is able to improve on the current best lower bounds for 12 out of the 21 benchmark instances, and to prove optimality for 6 of them. These new lower bounds are useful to estimate the quality of the upper bounds obtained with various heuristic approaches.
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ISSN:0377-2217
1872-6860
DOI:10.1016/j.ejor.2011.02.019