A comparative analysis on solving university departmental course allocation problem using AI optimization algorithms

Abstract

This paper discusses about various types of constraints, regulations, difficulties and solutions to overcome the challenges regarding university departmental course allocation problem. A CSP solver algorithm, Genetic Algorithm, Simulated Annealing and a hybrid of Genetic Algorithm and Simulated Annealing has been used separately to generate the best course assignment and also to compare the results generated by these four algorithms. The Department of Computer Science and Engineering of BRAC University has been used as a case study to discover the scope of automation in this research. After analyzing the information gathered from the department itself, some constraints were formulated. These constraints manage to cover all the aspects needed to be kept in mind while preparing a class schedule for a faculty member without any clashes. The goal is to generate optimized solution(s) which will fulfill those constraints. At this point, the main focus is on the perspective of the faculty members but in the near future, there will be enough opportunities for expansions, like focusing on the lab change procedure of the students, assignment of student tutors and many more.