A Hybrid Evolutionary Approach to Solve University Course Allocation Problem

TL;DR

This paper presents a hybrid evolutionary algorithm combining local repair and genetic algorithms to optimize university course allocation, effectively handling constraints and reducing manual workload.

Contribution

It introduces a novel hybrid approach specifically designed for university course scheduling, improving efficiency and accuracy over existing methods.

Findings

The hybrid algorithm outperforms baseline optimization algorithms in accuracy.

It reduces scheduling time compared to manual and traditional methods.

The approach effectively manages complex constraints in course allocation.

Abstract

This paper discusses various types of constraints, difficulties and solutions to overcome the challenges regarding university course allocation problem. A hybrid evolutionary algorithm has been defined combining Local Repair Algorithm and Modified Genetic Algorithm to generate the best course assignment. After analyzing the collected dataset, all the necessary constraints were formulated. These constraints manage to cover the aspects needed to be kept in mind while preparing clash free and efficient class schedules for every faculty member. The goal is to generate an optimized solution which will fulfill those constraints while maintaining time efficiency and also reduce the workload of handling this task manually. The proposed algorithm was compared with some base level optimization algorithms to show the better efficiency in terms of accuracy and time.