1 / 2016-10-28 21:12:02

Scalable Benchmark Dataset for Examination Timetable with Constraint Satisfaction Modeling

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Examination timetable is a complex administrative task at educational institutions that must fulfill various constraints to generate the examination timetable to schedule exam sessions within a precise period. The examination timetable problem is a real-life NP–Hard combinatorial optimization problem. It could be modeled as Constraint Satisfaction Problems (CSPs). Furthermore, Constraint Logic Programming (CLP) approach could particularly investigate the exam timetable problems. This paper presents a real examination scalable dataset of the Community College (CC), University of Sharjah (UoS). The dataset has an increasing number of student enrollments, wider variety of courses, accumulative number of combined courses, low number of exam halls, very limited timeslots, low number of invigilators and distant branches. Therefore, it is a challenge to contribute a solution for examination timetabling problem with this dataset. This dataset has more practical constraints than the current benchmark datasets. Therefore, we hoped it could be used as future benchmark problem because of its complexity. This paper applies the Optimization Programming Language (OPL) to model the examination timetable problem using the CSP definition as well as solving this model using a CLP Solver.