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.
['J. Joshua Thomas', 'Ahamad Tajudin Khader', 'Bahari Belaton', 'Amy Leow']
Exploration of rough sets analysis in real-world examination timetabling problem instances