Abstract
The aim of this paper is to present a model to support the decision making process on the Student Quota Problem (i.e., the maximum number of students that could be admitted in a university program). The number of students attended by universities is a key factor of national and international policies. The Organization for Economic Co-operation and Development (OECD) and Colombian official entities use this indicator to define goals of the educational level of young population. However, while the expectations of increasing the number of attended students are high, there are limits of growth based on resource limitations.
We introduce the Student Quota Problem as the decision on the maximum number of students that could be admitted in a university career over time, given a set of constraints on institutional resources. We propose a Mixed Integer Programming model (MIP) and a series of linear constraints related to the facility maximum capacity, students dropout and graduation percentages, faculty professors, number and type of courses to cover the students demand. Preliminary experimentations with data related to an undergraduate program of a Colombian university, showed results that can be used to determine upper and lower bounds on the number of admitted students, required professors, number and type of courses, and the required infrastructure capacity in terms of availability hours.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Agencia de Noticias UN: Cobertura del 75% en educación superior, apuesta de la nación para 2025 (2017). https://agenciadenoticias.unal.edu.co/detalle/article/cobertura-del-75-en-educacion-superior-apuesta-de-la-nacion-para-2025.html
Al-Yakoob, S.M., Sherali, H.D.: Mathematical programming models and algorithms for a class-faculty assignment problem. Eur. J. Oper. Res. 173(2), 488–507 (2006)
Al-Yakoob, S.M., Sherali, H.D.: A column generation mathematical programming approach for a class-faculty assignment problem with preferences. Comput. Manag. Sci. 12(2), 297–318 (2015)
Brailsford, S.C., Potts, C.N., Smith, B.M.: Constraint satisfaction problems: algorithms and applications. Eur. J. Oper. Res. 119(3), 557–581 (1999)
Burke, E., Kendall, G., Newall, J., Hart, E., Ross, P., Schulenburg, S.: Hyper-heuristics: an emerging direction in modern search technology. In: Glover, F., Kochenberger, G.A. (eds.) Handbook of metaheuristics, pp. 457–474. Springer, Boston (2003). https://doi.org/10.1007/0-306-48056-5_16
Burke, E.K., Petrovic, S., Qu, R.: Case-based heuristic selection for timetabling problems. J. Sched. 9(2), 115–132 (2006). https://doi.org/10.1007/s10951-006-6775-y
Castañeda Valle, R., Rebolledo Gómez, C.: Panorama de la educación: Indicadores de la ocde. Nota del País, pp. 1–11 (2013)
Chin-Ming, H., Chao, H.M.: A heuristic based class-faculty assigning model with the capabilities of increasing teaching quality and sharing resources effectively. Comput. Sci. Inf. Eng. 4(1), 740–744 (2009)
Head, C., Shaban, S.: A heuristic approach to simultaneous course/student timetabling. Comput. Oper. Res. 34(4), 919–933 (2007). https://doi.org/10.1016/j.cor.2005.05.015. http://www.sciencedirect.com/science/article/pii/S0305054805001656
Hsu, C.M., Chao, H.M.: A student-oriented class-course timetabling model with the capabilities of making good use of student time, saving college budgets and sharing departmental resources effectively, vol. 2, pp. 379–384 (2009)
Oktavia, M., Aman, A., Bakhtiar, T.: Courses timetabling problem by minimizing the number of less preferable time slots. In: IOP Conference Series: Materials Science and Engineering, vol. 166, p. 012025. IOP Publishing (2017)
Petrovic, S., Burke, E.K.: University timetabling (2004)
Petrovic, S., Bykov, Y.: A multiobjective optimisation technique for exam timetabling based on trajectories. In: Burke, E., De Causmaecker, P. (eds.) PATAT 2002. LNCS, vol. 2740, pp. 181–194. Springer, Heidelberg (2003). https://doi.org/10.1007/978-3-540-45157-0_12
Pochet, Y., Wolsey, L.A.: Production Planning by Mixed Integer Programming. Springer, New York (2006). https://doi.org/10.1007/0-387-33477-7
Smith, J.C., Taskin, Z.C.: A tutorial guide to mixed-integer programming models and solution techniques. In: Optimization in Medicine and Biology, pp. 521–548 (2008)
SNIES - Ministry of National Education in Colombia: Resumen de indicadores de educación superior (2016). https://www.mineducacion.gov.co/sistemasdeinformacion/1735/w3-article-212350.html
Vielma, J.P.: Mixed integer linear programming formulation techniques. SIAM Rev. 57(1), 3–57 (2015)
Wren, A.: Scheduling, timetabling and rostering—a special relationship? In: Burke, E., Ross, P. (eds.) PATAT 1995. LNCS, vol. 1153, pp. 46–75. Springer, Heidelberg (1996). https://doi.org/10.1007/3-540-61794-9_51
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Switzerland AG
About this paper
Cite this paper
Duque, R., Bucheli, V., Aranda, J.A., Díaz, J.F. (2018). Making Decisions on the Student Quota Problem: A Case Study Using a MIP Model. In: Serrano C., J., Martínez-Santos, J. (eds) Advances in Computing. CCC 2018. Communications in Computer and Information Science, vol 885. Springer, Cham. https://doi.org/10.1007/978-3-319-98998-3_28
Download citation
DOI: https://doi.org/10.1007/978-3-319-98998-3_28
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-98997-6
Online ISBN: 978-3-319-98998-3
eBook Packages: Computer ScienceComputer Science (R0)