[1] Aladag, C.H., Hocaoglu, G., and Basaran M.A. (2009). “The effect of neighborhood structures on tabu search algorithm in solving course timetabling problem”, Expert Systems with Applications, 36, pp. 12349–12356.
[2] Al-Yakoob, S.M., and Sherali, H.D. (2007). “A mixed-integer programming approach to a class timetabling problem: A case study with gender policies and traffic considerations”, European Journal of Operational Research, 180, pp. 1028–1044.
[3] Bardadym, V.A. (1996). “Computer-aided school and university timetabling: The new wave”. In E. Burke and P. Ross (Eds.), Practice and theory of automated timetabling. Lecture notes in computer science, 1153, pp. 22–45. Berlin: Springer.
[4] Boland, N., Hughes, B.D., Merlot, L.T.G., and Stuckey P.J. (2008). “New integer linear programming approaches for course timetabling”, Computers and Operations Research, 35, pp. 2209–2233.
[5] Burke, E.K., Eckersley, A.J., McCollum, B., Petrovic, S., and Qu R. (2010). “Hybrid variable neighbourhood approaches to university exam timetabling”, European Journal of Operational Research, 206, pp. 46–53.
[6] Burke, E.K., McCollum, B., Meisels, A., Petrovic, S., and Qu, R. (2007). “A graph-based hyper-heuristic for educational timetabling problems”, European Journal of Operational Research, 176, pp. 177–192.
[7] Causmaecker, P.D., Demeester P., and Vanden Berghe, G. (2009). “A decomposed metaheuristic approach for a real-world university timetabling problem”, European Journal of Operational Research, 195, pp. 307–318.
[8] Daskalaki, S., and Birbas, T. (2005). “Efficient solutions for a university timetabling problem through integer programming”, European Journal of Operational Research, 160, pp. 106–120.
[9] Dimopoulou, M., and Miliotis, P. (2004). “An automated university course timetabling system developed in a distributed environment: A case study”, European Journal of Operational Research, 153, pp. 136–147.
[10] Flesza, K., and Hindi, K.S. (2004). “Solving the resource-constrained project scheduling problem by a variable neighborhood search”, European Journal of Operational Research, 155, pp. 402–413.
[11] Hansen, P., and Mladenovic, N. (2001). “Variable neighborhood search: principles and applications”, European Journal of Operational Research, 130, pp. 449–467.
[12] Liao, C.J., and Cheng, C.C. (2007). “A variable neighborhood search for minimizing single machine weighted earliness and tardiness with common due date”, Computers and Industrial Engineering, 52, pp. 404–413.
[13] Lü, Z., and Hao, J.K. (2010). “Adaptive Tabu Search for course timetabling”, European Journal of Operational Research, 200, pp. 235–244.
[15] Mladenovic, N., and Hansen, P. (1997). “Variable neighborhood search”, Computers and Operations Research, 24, pp. 1097–1100.
[16] MirHassani, S.A. (2006). “A computational approach to enhancing course timetabling with integer programming”, Applied Mathematics and Computation, 175, pp. 814–822.
[18] Shiau, D.F. (2011). “A hybrid particle swarm optimization for a university course scheduling problem with flexible preferences”, Expert Systems with Applications, 38, pp. 235–248.
[19] Turabieh, H., Abdullah, S. (2011). “An integrated hybrid approach to the examination time tabling problem”, Omega, 39, pp. 598–607.
[20] Wang, Y.Z. (2002). “An application of genetic algorithm methods for teacher assignment problems”, Expert Systems with Applications, 22, pp. 295–302.
[21] Wang, Y.Z. (2003). “Using genetic algorithm methods to solve course scheduling problems”, Expert Systems with Applications, 25, pp. 39-50.
[22] Zhang, D., Liu, Y., M’Hallah, R., and Leung, S.C.H. (2010). “A simulated annealing with a new neighborhood structure based algorithm for high school timetabling problems”, European Journal of Operational Research, 203, pp. 550–558.
[23] Atashpaz-Gargari, E., and Lucas, C., (2007). Imperialist competitive algorithm: an algorithm for optimization inspired by imperialistic competition. IEEE Congress Evolutionary Computers, Singapore, pp. 4661–4667.
[24] Atashpaz-Gargari, E., Hashemzadeh, F., Rajabioun, R., and Lucas, C., (2008). Colonial competitive algorithm, a novel approach for PID controller design in MIMO distillation column process. International Journal of Intelligent Computation and Cyberntic, 1, pp. 337–355.
[25] Bagher, M., Zandieh, M., and Farsijani, H., (2010). Balancing of stochastic U-type assembly lines: an imperialist competitive algorithm. International Journal of Advanced Manufacturing Technology, 54, pp. 271–285.
[27] Zhou, W., Yan, J., Li, Y., Xia, C., and Zheng, J., (2013). Imperialist competitive algorithm for assembly sequence planning. International Journal of Advanced Manufacturing Technology, 67, pp. 2207-2216.
[28] Kolon, M., (1999). Some new results on simulated annealing applied to the job shop scheduling problem. European Journal of Operational Research, 113, pp. 123–136.
[29] Naderi, B., Zandieh, M., Khaleghi Ghoshe Balagh, A., and Roshanaei, V., (2009). “An improved simulated annealing for hybrid flowshops with sequence-dependent setup and transportation times to minimize total completion time and total tardiness”, Expert Systems with Applications, 36, pp. 9625–9633.
[30] Kahar, M.N.M., and Kendall, G. (2010). “The examination timetabling problem at Universiti Malaysia Pahang: Comparison of a constructive heuristic with an existing software solution”, European Journal of Operational Research, 207, pp. 557–565.