Allocating Students to Industry Placements Using Integer Programming and Ant Colony Optimisation
| Autor: | Dhananjay R. Thiruvady, Kerri Morgan, Asef Nazari, Susan E. Bedingfield |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Matching (statistics)
Operations research Computer science Industrial engineering. Management engineering 0211 other engineering and technologies Student engagement 02 engineering and technology T55.4-60.8 01 natural sciences Theoretical Computer Science allocation industry placements ant colony optimisation 0101 mathematics Integer programming integer programming Numerical Analysis 021103 operations research Heuristic matching 010102 general mathematics Rank (computer programming) QA75.5-76.95 Ant colony Computational Mathematics Computational Theory and Mathematics Electronic computers. Computer science Scalability Diversity (business) |
| Zdroj: | Algorithms, Vol 14, Iss 219, p 219 (2021) Algorithms Volume 14 Issue 8 |
| ISSN: | 1999-4893 |
| Popis: | The increasing demand for work-ready students has heightened the need for universities to provide work integrated learning programs to enhance and reinforce students’ learning experiences. Students benefit most when placements meet their academic requirements and graduate aspirations. Businesses and community partners are more engaged when they are allocated students that meet their industry requirements. In this paper, both an integer programming model and an ant colony optimisation heuristic are proposed, with the aim of automating the allocation of students to industry placements. The emphasis is on maximising student engagement and industry partner satisfaction. As part of the objectives, these methods incorporate diversity in industry sectors for students undertaking multiple placements, gender equity across placement providers, and the provision for partners to rank student selections. The experimental analysis is in two parts: (a) we investigate how the integer programming model performs against manual allocations and (b) the scalability of the IP model is examined. The results show that the IP model easily outperforms the previous manual allocations. Additionally, an artificial dataset is generated which has similar properties to the original data but also includes greater numbers of students and placements to test the scalability of the algorithms. The results show that integer programming is the best option for problem instances consisting of less than 3000 students. When the problem becomes larger, significantly increasing the time required for an IP solution, ant colony optimisation provides a useful alternative as it is always able to find good feasible solutions within short time-frames. |
| Databáze: | OpenAIRE |
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