Variable Neighborhood Search for the University Lecturer-Student Assignment Problem

TL;DR

This paper explores the use of variable neighborhood search heuristics to optimize the assignment of students to university research topics, considering preferences and workload balancing, demonstrating superior results over other methods.

Contribution

It introduces a novel application of variable neighborhood search to a university assignment problem, including an extension with a second objective for workload balancing.

Findings

Variable neighborhood search outperforms other heuristics on tested instances.

The approach effectively handles real-world and generated data.

The method supports decision-making in academic assignment problems.

Abstract

The paper presents a study of local search heuristics in general and variable neighborhood search in particular for the resolution of an assignment problem studied in the practical work of universities. Here, students have to be assigned to scientific topics which are proposed and supported by members of staff. The problem involves the optimization under given preferences of students which may be expressed when applying for certain topics. It is possible to observe that variable neighborhood search leads to superior results for the tested problem instances. One instance is taken from an actual case, while others have been generated based on the real world data to support the analysis with a deeper analysis. An extension of the problem has been formulated by integrating a second objective function that simultaneously balances the workload of the members of staff while maximizing utility of the students. The algorithmic approach has been prototypically implemented in a computer system. One important aspect in this context is the application of the research work to problems of other scientific institutions, and therefore the provision of decision support functionalities.