Optimizing Satisfaction in a Multi-courses Allocation Problem
The resource allocation problem is a traditional kind of NP-hard problem. One of its application domains is the allocation of educational resources. In most universities, students select some courses they would like to attend by ranking the proposed courses. However, to ensure the quality of a course, the number of seats is limited, so not all students can enroll in their preferred courses. Therefore, the school administration needs some mechanism to assign the available resources. In this paper, the course allocation problem has been modeled as a Constraint Satisfaction Optimization Problem (CSOP) and two metrics have been defined to quantify the satisfaction of students. The problem is solved with Gecode and a greedy-based algorithm showing how the CSOP approach is able to allocate resources optimizing the students’ satisfaction. Another contribution of this work is the allocation of several courses simultaneously, generating feasible solutions in a short time. The allocation procedures are based on preferences for courses defined by students, and on the administration’s constraints at Ecole Hôtelière de Lausanne. Ten data sets have been generated using the distribution of students’ preferences for courses, and have been used to carry out a complete experimental analysis.
This work has been supported by CIBERDINE Project (S2013/ICE-3095) and by Savier Project (Airbus Defence&Space, FUAM-076915). The authors would also like to thank Vincent Maronnier for his contribution.
- 1.Budish, E.B., Cantillon, E.: (2010). The multi-unit assignment problem: theory and evidence from course allocation at HarvardGoogle Scholar
- 2.Cano, J.I., Sánchez, L., Camacho, D., Pulido, E., Anguiano, E.: Allocation of educational resources through happiness maximization. In: Proceedings of the 4th International Conference on Software and Data Technologies (2009)Google Scholar
- 3.Cano, J.I., Sánchez, L., Camacho, D., Pulido, E., Anguiano, E.: Using Preferences to solve student-class allocation problem. In: Intelligent Data Engineering and Automated Learning-IDEAL 2009, pp. 626–632. Springer, Berlin (2009)Google Scholar
- 5.Nogareda, A.M., Camacho, D.: Integration of Ant Colony Optimization algorithms with Gecode. In: 20th International Conference on Principles and Practice of Constraint Programming (CP 2014): Doctoral Program Proceedings, 59–64 September 2014Google Scholar
- 6.Francesca, R., Venable, K.B., Walsh, T.: A Short introduction to preferences: between artificial intelligence and social choice. In: Synthesis Lectures on Artificial Intelligence and Machine Learning 5.4, pp. 1–102 (2011)Google Scholar
- 7.Schulte, C., Lagerkvist, M., Tack, G.: Gecode. Software download and online material at the website: http://www.gecode.org. Downloaded in May 2014
- 9.Tsang, E.: Foundations of constraint satisfaction, vol. 289. Academic press, London (1993)Google Scholar