A simulated annealing approach to the student-project allocation problem

TL;DR

This paper presents a simulated annealing method for efficiently solving the student-project allocation problem, optimizing student satisfaction while respecting project and supervisor constraints, demonstrated on real university data.

Contribution

The paper introduces a novel application of simulated annealing to the student-project allocation problem, achieving high-quality solutions rapidly compared to traditional trial-and-error methods.

Findings

High-quality solutions achieved within minutes.

Solution quality comparable to laborious manual methods.

Effect of project-to-student ratio on allocation quality analyzed.

Abstract

We describe a solution to the student-project allocation problem using simulated annealing. The problem involves assigning students to projects, where each student has ranked a fixed number of projects in order of preference. Each project is offered by a specific supervisor (or supervisors), and the goal is to find an optimal matching of students to projects taking into account the students' preferences, the constraint that only one student can be assigned to a given project, and the constraint that supervisors have a maximum workload. We show that when applied to a real dataset from a university physics department, simulated annealing allows the rapid determination of high quality solutions to this allocation problem. The quality of the solution is quantified by a satisfaction metric derived from empirical student survey data. Our approach provides high quality allocations in a matter of minutes that are as good as those found previously by the course organizer using a laborious trial-and-error approach. We investigate how the quality of the allocation is affected by the ratio of the number of projects offered to the number of students and the number of projects ranked by each student. We briefly discuss how our approach can be generalized to include other types of constraints and discuss its potential applicability to wider allocation problems.