# A near Pareto optimal approach to student-supervisor allocation with two sided preferences and workload balance

## TL;DR

This paper presents a multi-objective genetic algorithm for student-supervisor allocation that considers preferences, quotas, and workload balance, achieving near Pareto optimal solutions efficiently.

## Contribution

It introduces novel genetic operators and demonstrates the effectiveness of a multi-objective genetic algorithm for complex student-supervisor assignment problems.

## Key findings

- The proposed genetic algorithm outperforms classic methods.
- It produces near Pareto optimal allocations within reasonable time.
- The algorithm effectively balances preferences, quotas, and workload.

## Abstract

The problem of allocating students to supervisors for the development of a personal project or a dissertation is a crucial activity in the higher education environment, as it enables students to get feedback on their work from an expert and improve their personal, academic, and professional abilities. In this article, we propose a multi-objective and near Pareto optimal genetic algorithm for the allocation of students to supervisors. The allocation takes into consideration the students and supervisors' preferences on research/project topics, the lower and upper supervision quotas of supervisors, as well as the workload balance amongst supervisors. We introduce novel mutation and crossover operators for the student-supervisor allocation problem. The experiments carried out show that the components of the genetic algorithm are more apt for the problem than classic components, and that the genetic algorithm is capable of producing allocations that are near Pareto optimal in a reasonable time.