Greedy Ants Colony Optimization Strategy for Solving the Curriculum Based University Course Timetabling Problem

TL;DR

This paper introduces a greedy ant colony optimization algorithm for university course timetabling, demonstrating its effectiveness on the ITC 2007 dataset by finding feasible and near-optimal solutions, and establishing a benchmark for future ant-based methods.

Contribution

It presents a novel ant colony optimization strategy specifically designed for curriculum-based university course timetabling, filling a gap in the application of meta-heuristics to this problem.

Findings

Successfully finds feasible solutions for all dataset instances.

Achieves near-optimal solutions in some cases.

Performs better than some existing approaches, but not the best overall.

Abstract

Timetabling is a problem faced in all higher education institutions. The International Timetabling Competition (ITC) has published a dataset that can be used to test the quality of methods used to solve this problem. A number of meta-heuristic approaches have obtained good results when tested on the ITC dataset, however few have used the ant colony optimization technique, particularly on the ITC 2007 curriculum based university course timetabling problem. This study describes an ant system that solves the curriculum based university course timetabling problem and the quality of the algorithm is tested on the ITC 2007 dataset. The ant system was able to find feasible solutions in all instances of the dataset and close to optimal solutions in some instances. The ant system performs better than some published approaches, however results obtained are not as good as those obtained by the best published approaches. This study may be used as a benchmark for ant based algorithms that solve the curriculum based university course timetabling problem.