Stochastic Search Algorithms for Exam Scheduling

Abstract

Scheduling final exams for large numbers of courses and students in universities is an intractable problem. Where scheduling is done manually, conflicts and unfairness are inevitable. Conflicts occur when simultaneous exams are scheduled for the same student, and unfairness to a student refers to consecutive exams or more than two exams on the same day. A good exam schedule should aim to minimize conflicts and the two unfairness factors based on user-assigned weights to these three factors and subject to some constraints such as classrooms’ number and capacities. In this work, we use a modified weighted-graph coloring problem formulation and adapt two stochastic search algorithms for solving the problem. The two algorithms are a simulated annealing algorithm (SA) and a genetic algorithm (GA). We also propose an improvement to a ‘good’ clustering-based heuristic procedure, known as FESP, by using simulated annealing procedures. The improved heuristic is referred to as FESP-SA. Then, we empirically compare the three proposed algorithms and FESP using realistic data. Our experimental results show that SA and GA produce good exam schedules that are better than those of FESP heuristic procedure. Also, SA and GA allow a reduction in the number of exam days without much aggravating conflicts and unfairness. However, SA is more favorable since it is faster than GA.