Abstract
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.
Highlights
There is an increasing emphasis on Work Integrated Learning (WIL) in universities and other tertiary education providers [1] As a result, the number of students undertaking WIL is growing rapidly, and the need to effectively manage the processes involved is increasingly important
There are two aspects to investigate in the second set of experiments. These are to test how the Integer programming (IP) model scales with problem size and to see how the performance varies with respect to the ratio of the number of students to the number of companies
We report the average allocation (Avg.), the number of first preferences assigned (#1 s) and the number of last preferences assigned (#3 s). (Note: the IP model does not allow these by implementing it as a hard constraint.) For the IP model, the average allocation (Avg.), number of first preferences (#1 s) and time taken in seconds (Time (s)) to allocate are reported
Summary
There is an increasing emphasis on Work Integrated Learning (WIL) in universities and other tertiary education providers [1] As a result, the number of students undertaking WIL is growing rapidly, and the need to effectively manage the processes involved is increasingly important. Compared to previous studies that investigate similar problems, the IBL problem is unique in that it considers aspects such as partner happiness and gender equity To solve this problem, we develop an exact method (IP) and heuristic method (ACO). We generate problem instances with varying numbers of students, companies and placements, which are generated based on data for previous semesters These problem instances allow testing of the algorithms for scalability and provide insight into how the algorithms would perform on allocation problems such as those arising in other domains that have much larger allocation requirements (for example, nursing).
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