A survey of practical applications of examination timetabling algorithms
Operations Research
Communications of the ACM
Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
Rough-Neuro-Computing: Techniques for Computing with Words
Rough-Neuro-Computing: Techniques for Computing with Words
A Hybrid Genetic Algorithm for Highly Constrained Timetabling Problems
Proceedings of the 6th International Conference on Genetic Algorithms
Examination Timetabling in British Universities: A Survey
Selected papers from the First International Conference on Practice and Theory of Automated Timetabling
New Algorithms for Examination Timetabling
WAE '00 Proceedings of the 4th International Workshop on Algorithm Engineering
INFORMS Journal on Computing
A parallel coordinates visualization for the uncapaciated examination timetabling problem
IVIC'11 Proceedings of the Second international conference on Visual informatics: sustaining research and innovations - Volume Part I
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The examination timetabling problem is widely studied and a major activity for academic institutions. In real world cases, an increasing number of student enrolments, variety of courses throw in the growing challenge in the research with a wider range of constraints. Many optimization problems are concerned with the best feasible solution with minimum execution time of algorithms. The aim of this paper is to propose rough sets methods to investigate the Carter datasets. Two rough sets (RS) approaches are used for the data analysis. Firstly, the discretization process (DP) returns a partition of the value sets into intervals. Secondly the rough sets Boolean reasoning (RSBR) achieves the best decision table on the large data instances. The rough sets classified datasets are experimented with an examination scheduler. The improvements of the solutions on Car-s-91 and Car-f-91 datasets are reported.