# Allocating Students to Industry Placements Using Integer Programming and Ant Colony Optimisation
## 8 Conclusions and Future Work  
In this study, we investigate the problem of allocating students to industry placements, which we refer to as the IBL allocation problem. This problem differs from those in previous studies by considering multiple requirements of the companies, such as company preferences for students, gender mix and sector constraints. We propose an exact approach to solve the problem via integer programming. We find that although the IP model is very effective on real data, it has limitations in the way it scales with large problem sizes. Hence, we also propose an ACO-based heuristic with the aim of finding good solutions in reasonable time-frames. Compared to manual allocations on real instances since 2012, we see that the IP model improves upon these allocations in several respects, including the average allocation preference and the number of placements allocated their #1 preferences. This study demonstrates the value of automation in industry placement programs at universities by providing excellent solutions with a high degree of flexibility. The real data show a trend of increasing numbers of students who would like to participate in IBL placements. Moreover, other university departments (e.g., nursing) typically require much larger numbers of placements (up to 4000 placements at some institutions). This provides the motivation to investigate how the proposed approaches deal with increasing scales. Hence, based on information from the real data (e.g., distribution of preferences), we generated a number of artificial problem instances. The IP solves small to medium problems very effectively, finding optimal solutions within 30 min, but struggles when the instances consist of more than 3000 students and may fail to find solutions for the largest instances. For these problem instances, we see a clear advantage provided by ACO, where it is able to find good solutions in small time-frames, although optimality cannot be proved. Future WorkFor the IBL placement allocation, the IP model is a viable option and scales well for the current program (\(<\)200 students per placement cycle). As such, it will be interesting to investigate whether or not the model can easily be modified to deal with industry placement programs at other universities. The authors of this study are currently investigating possibilities in this direction. For very large similar allocation problems (such as allocating students to nursing placements), ACO is the preferable option, but with no guarantee of optimality. This leads to several potential research directions, where the IP and ACO approaches may be further enhanced to provide improved solutions in short time-frames. In particular, IP-based decompositions, such as Lagrangian relaxation [37; 38], have proved to be effective in the past  
Figure 4: The average allocation for \(\mathcal{MM}\)AS split by proportion of placements to students [0.25, 0.5, 0.75, 1.00]. and combinations of IPs and ACO have also been proven on a range of problems [39; 40; 41; 42]. Moreover, large neighbourhood search approaches [43; 44] may prove to be very effective in this context. Additionally, parallel implementations of ACO and associated hybrids [45; 46] have proved to be effective in the past and such approaches could help to reduce run-times, enabling good solutions for large problems to be found in under a minute. Conceptualization, D.T. ; Data curation, K.M. and S.B. ; Methodology, D.T., K.M. and A.N. ; Software, D.T. ; Validation, A.N. ; Writing - original draft, D.T., K.M. and S.B. ; Writing--review & editing, S.B. and A.N. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Not applicable. This research was supported in part by the Monash eResearch Centre and eSolutions-Research Support Services through the use of the MonARCH HPC Cluster. Exemption from Ethical Review Project Number 12409, MUHREC, 15 March 2018. Deakin University Ethics Exemption: 2018-403: Work Integrated learning applications.