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Allocating students to industry placements using integer programming and ant colony optimisation

Thiruvady, Dhananjay, Morgan, Kerri, Bedingfield, Susan and Nazari, Asef 2021, Allocating students to industry placements using integer programming and ant colony optimisation, Algorithms, vol. 14, no. 8, pp. 1-17, doi: 10.3390/a14080219.

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Title Allocating students to industry placements using integer programming and ant colony optimisation
Author(s) Thiruvady, DhananjayORCID iD for Thiruvady, Dhananjay orcid.org/0000-0002-8011-933X
Morgan, KerriORCID iD for Morgan, Kerri orcid.org/0000-0002-7263-8340
Bedingfield, Susan
Nazari, AsefORCID iD for Nazari, Asef orcid.org/0000-0003-4955-9684
Journal name Algorithms
Volume number 14
Issue number 8
Article ID 219
Start page 1
End page 17
Total pages 17
Publisher MDPI AG
Place of publication Basel, Switzerland
Publication date 2021
ISSN 1999-4893
Summary 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
Language eng
DOI 10.3390/a14080219
Indigenous content off
Field of Research 01 Mathematical Sciences
08 Information and Computing Sciences
09 Engineering
HERDC Research category C1 Refereed article in a scholarly journal
Free to Read? Yes
Persistent URL http://hdl.handle.net/10536/DRO/DU:30153747

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Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.