IBM. IBM CPLEX Optimizer. 2017 03/02/2017]; Available from: http://www.ibm.com/software/commerce/optimization/cplex-optimizer/.

COTTAPP. COTTAPP: Course Timetabling Applicatoin. 2017; Available from: http://ieportal.hacettepe.edu.tr/apps/COTTApp/.

Akkoyunlu, E.A., A linear algorithm for computing the optimum university timetable. The Computer Journal, 1973. 16(4): p. 347-350.

Schniederjans, M.J. and G.C. Kim, A goal programming model to optimize departmental preference in course assignments. Computers & Operations Research, 1987. 14(2): p. 87-96.

Dinkel, J.J., J. Mote, and M. Venkataramanan, Or practice—An efficient decision support system for academic course scheduling. Operations Research, 1989. 37(6): p. 853-864.

Dowsland, K.A., A timetabling problem in which clashes are inevitable. Journal of the Operational Research Society, 1990. 41(10): p. 907-918.

Costa, D., A tabu search algorithm for computing an operational timetable. European Journal of Operational Research, 1994. 76(1): p. 98-110.

Colorni, A., M. Dorigo, and V. Maniezzo, Metaheuristics for high school timetabling. Computational optimization and applications, 1998. 9(3): p. 275-298.

Abramson, D., M.K. Amoorthy, and H. Dang, Simulated annealing cooling schedules for the school timetabling problem. Asia-Pacific Journal of Operational Research, 1999. 16(1): p. 1.

Badri, M.A., et al., A multi-objective course scheduling model: Combining faculty preferences for courses and times. Computers & operations research, 1998. 25(4): p. 303-316.

Deris, S., S. Omatu, and H. Ohta, Timetable planning using the constraint-based reasoning. Computers & Operations Research, 2000. 27(9): p. 819-840.

Burke, E.K. and S. Petrovic, Recent research directions in automated timetabling. European Journal of Operational Research, 2002. 140(2): p. 266-280.

Smith, K.A., D. Abramson, and D. Duke, Hopfield neural networks for timetabling: formulations, methods, and comparative results. Computers & industrial engineering, 2003. 44(2): p. 283-305.

Carrasco, M.P. and M.V. Pato, A comparison of discrete and continuous neural network approaches to solve the class/teacher timetabling problem. European Journal of Operational Research, 2004. 153(1): p. 65-79.

Daskalaki, S., T. Birbas, and E. Housos, An integer programming formulation for a case study in university timetabling. European Journal of Operational Research, 2004. 153(1): p. 117-135.

Daskalaki, S. and T. Birbas, Efficient solutions for a university timetabling problem through integer programming. European Journal of Operational Research, 2005. 160(1): p. 106-120.

Al-Yakoob, S.M. and H.D. Sherali, Mathematical programming models and algorithms for a class–faculty assignment problem. European Journal of Operational Research, 2006. 173(2): p. 488-507.

MirHassani, S., A computational approach to enhancing course timetabling with integer programming. Applied Mathematics and Computation, 2006. 175(1): p. 814-822.

Hao, J.-K. and U. Benlic, Lower bounds for the ITC-2007 curriculum-based course timetabling problem. European Journal of Operational Research, 2011. 212(3): p. 464-472.

Benli, O.S. and A. Botsali, An optimization-based decision support system for a university timetabling problem: An integrated constraint and binary integer programming approach. Computers and Industrial Engineering, 2004: p. 1-29.

Bakır, M.A. and C. Aksop, A 0-1 integer programming approach to a university timetabling problem. Hacettepe Journal of Mathematics and Statistics, 2008. 37(1): p. 41-55.

R Development Core Team. R: A language and environment for statistical computing. 2017 03/02/2017]; Available from: http://www.R-project.org.

RStudio. Shiny: A web application framework for R. 2017 03/02/2017]; Available from: https://shiny.rstudio.com/.