Abstract
The resource allocation problem and the timetabling problem are traditional kinds of NP-hard problems. Both problems can be found in universities where students can select courses they would like to attend before or after the timetabling is done. When demand exceeds capacity, the universities may allocate the available seats independently from the timetabling, but students may have then to decide which courses they are going to attend because of clashes in their timetable. To avoid this situation, some universities prepare their timetable considering students selection. In addition to that, students may submit preferences over courses, and the school administration has to assign seats and do the timetable considering both preferences and clashes. In this paper, both problems, seats allocation and timetabling, have been modeled separately and combined as constraint satisfaction optimization problems (CSOP). Two algorithms have been designed and implemented to find a solution to both problems simultaneously maximizing the satisfaction of students using a CSOP solver and an Ant colony optimization algorithm for the timetabling problem. The results of both algorithms are then compared. The allocation and timetabling procedures are based on preferences for courses defined by students, and on the administration’s constraints at Ecole Hôtelière de Lausanne. Three real data sets have been used to carry out a complete experimental analysis. High-quality solutions are obtained in a few minutes with both approaches; those solutions are currently used at Ecole Hôtelière de Lausanne.
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Agustín-Blas LE, Salcedo-Sanz S, Ortíz-García EG, Portilla-Figueras A, Pérez-Bellido ÁM (2009) A hybrid grouping genetic algorithm for assigning students to preferred laboratory groups. Expert Syst Appl 36(3):7234–7241. doi:10.1016/j.eswa.2008.09.020
Babaei H, Karimpour J, Hadidi A (2015) A survey of approaches for university course timetabling problem. Comput Ind Eng 86:43–59
Budish E, Cantillon E (2012) The multi-unit assignment problem: theory and evidence from course allocation at harvard. Am Econ Rev 102(5):2237–2271
Cano JI, Sánchez L, Camacho D, Pulido E, Anguiano E (2009) Allocation of educational resources through happiness maximization. In: Proceedings of the 4th international conference on software and data technologies
Cano JI, Sánchez L, Camacho D, Pulido E, Anguiano E (2009) Using preferences to solve student–class allocation problem. In: Intelligent data engineering and automated learning-IDEAL 2009. Springer, Berlin, pp 626–632
de Werra D (1985) An introduction to timetabling. Eur J Oper Res 19(2):151–162
Di Gaspero L (2015) Integration of metaheuristics and constraint programming. In: Springer handbook of computational intelligence. Springer, Berlin, pp 1225–1237
Di Gaspero L, McCollum B, Schaerf A (2007) The second international timetabling competition (itc-2007): curriculum-based course timetabling (track 3). Tech. rep., Technical Report QUB/IEEE/Tech/ITC2007/CurriculumCTT/v1. 0, Queens University, Belfast, UK
Diebold F, Aziz H, Bichler M, Matthes F, Schneider A (2014) Course allocation via stable matching. Bus Inf Syst Eng 6(2):97–110
Dorigo M, Birattari M (2010) Ant colony optimization. In: Encyclopedia of machine learning. Springer, Berlin, pp 36–39
Kojima F (2013) Efficient resource allocation under multi-unit demand. Games Econ Behav 82:1–14
Landa-Torres I, Manjarres D, Salcedo-Sanz S, Ser JD, Gil-Lopez S (2013) A multi-objective grouping harmony search algorithm for the optimal distribution of 24-h medical emergency units. Expert Syst Appl 40(6):2343–2349. doi:10.1016/j.eswa.2012.10.051
Lewis R (2008) A survey of metaheuristic-based techniques for university timetabling problems. OR Spectrum 30(1):167–190
Lewis R, Paechter B, McCollum B et al (2007) Post enrolment based course timetabling: a description of the problem model used for track two of the second international timetabling competition. Cardiff Business School
McCollum B, Ireland N (2006) University timetabling: bridging the gap between research and practice. E Burke HR ed: PATAT pp 15–35
Nogareda AM, Camacho D (2014) Integration of ant colony optimization algorithms with gecode. In: Doctoral program proceedings of the 20th international conference on principles and practice of constraint programming
Nogareda AM, Camacho D (2016) Optimizing satisfaction in a multi-courses allocation problem. In: Intelligent distributed computing IX. Springer, Berlin, pp 247–256
Nothegger C, Mayer A, Chwatal A, Raidl GR (2012) Solving the post enrolment course timetabling problem by ant colony optimization. Ann Oper Res 194(1):325–339
Rossi F, Venable KB, Walsh T (2011) A short introduction to preferences: between artificial intelligence and social choice. Synth Lect Artif Intell Mach Learn 5(4):1–102
Schulte C, Tack G, Lagerkvist MZ (2010) Modeling and programming with gecode. http://www.gecode.org/doc/4.4.0/MPG.pdf. Accessed 3 Oct 2016
Socha K, Sampels M, Manfrin M (2003) Ant algorithms for the university course timetabling problem with regard to the state-of-the-art. In: Applications of evolutionary computing. Springer, Berlin, pp 334–345
Solnon C (2010) Ant colony optimization and constraint programming. Wiley Online Library
Solnon C (2002) Ants can solve constraint satisfaction problems. IEEE Trans Evolut Comput 6(4):347–357
Sönmez T, Ünver MU (2010) Course bidding at business schools*. Int Econ Rev 51(1):99–123
Stützle T, Hoos HH (2000) Max-min ant system. Future Gener Comput Syst 16(8):889–914
Tassopoulos IX, Beligiannis GN (2012) Using particle swarm optimization to solve effectively the school timetabling problem. Soft Comput 16(7):1229–1252
Tsang E (2014) Foundations of constraint satisfaction: the classic text. BoD–Books on Demand
Acknowledgments
This work has been supported by following research grants: Comunidad Autonoma de Madrid, under CIBERDINE S2013/ICE-3095 project, and EphemeCH (TIN2014-56494-C4-4-P) project, under Spanish Ministry of Economy and Competitivity, both supported by the European Regional Development Fund FEDER.
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Nogareda, AM., Camacho, D. Optimizing satisfaction in a multi-courses allocation problem combined with a timetabling problem. Soft Comput 21, 4873–4882 (2017). https://doi.org/10.1007/s00500-016-2375-8
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DOI: https://doi.org/10.1007/s00500-016-2375-8