Proposing Innovative Genetic Algorithms Model to Solve the Problem of the Professors' Educational Planning Considering Students' Opinions

Document Type: Research Paper

Authors

1 Msc, Social and Economic System Engineer, Specialized Data Mining Laboratory, Alzahra University, Tehran, Iran

2 Associate Prof. of Software Engineer, Faculty of Engineering, Alzahra University, Tehran, Iran

Abstract

Timing of curriculum planning for students and faculty could be done using diverse methods. The present research concerns with curriculum planning for professors considering the students' opinions. In doing so, the courses and the timing are determined based on the professors' common timetable, the professors' intensive courses timing and the class limitations. To achieve this goal, the genetic algorithm methodology was used in two steps. In the first stage, single-point cutting operator was used and in the second stage of the algorithm, a new intelligent operator called cyclic reverse list (RIL) was used provided that gold, silver and bronze time types were used for different courses. The advantages of this algorithm are using a new appropriate function (hot rolled), as well as new criteria and a new operator (RIL). Unlike conventional methods, in this method the appropriateness is considered in proportion with the whole population and we try to remove the impossible solutions. The optimal solution is chosen from among a multitude of provided responses. Therefore, it was found that we can reach the optimal solutions with regard to a better appropriateness.

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محمد، ب.، دهقانی، ت.، ذاکرتولایی، م. (1385). رویکردی نوین در زمان‎بندی دروس دانشگاه با استفاده از الگوریتم ژنتیک، دوازدهمین کنفرانس سالانۀ انجمن کامپیوتر ایران، تهران، دانشگاه شهید بهشتی.

Abdennadher, S. & Aly, M. (2007). Constraint-Based University Timetabling for the German University in Cairo, 21st Workshop on (Constraint) Logic Programming, Würzburg, Germany.

Abdullah, S., Burke, E.K. & Collum, B. (2005). An investigation of variable neighbourhood search for university course timetabling. In: Proceedings of MISTA 2005. The 2nd Multidisciplinary Conference on Scheduling: Theory and Applications, 18-21 July, pp. 413-427.

Abdullah, S. & Ahmadi, S. & Dror, M. & Burke, E.K.  & McCollum, B. (2003). A Tabu based Large Neighbourhood Search Methodology for the Capacitated Examination Timetabling Problem. DOI: 10.1057/palgrave.jors.2602258.

Behdad, M. & Dehghan, T. & Zaker Tavalayee, M. (2007). A New Approach in University Timetabling by Using Genetic Algoritm. 12th Annual Conference of Iranian Computer Society, Tehran, Shahid Beheshti University.
(in Persian)

Sutar, S.R. & Bichkar, R.S. (2012). University Timetabling based on Hard Constraints using Genetic Algorithm, International Journal of Computer Applications, 42(15),1-7.

Burke, E.K., Newall, J.P. & Weare, R.F. (1996). A Memetic Algorithm for University Exam Timetabling, PATAT I, Edinburgh, UK, Lecture Notes in Computer Science 1153. Springer-Verlag. (Editors: E.K. Burke and P. Ross), pp. 3-21.

Dorigo, M.I. & Caro, G.D. (1999). Ant Colony Optimization: A new Meta-Heuristic. Available in: http://staff.washington.edu/paymana/swarm/dorigo99-cec.pdf.

Doshi, H. & Mittal, D. & Negpure, R. & Sunasra, M. (2015). Automatic timetable generation using genetic algorithm. International Journal of Advanced Research in Computer and Communication Engineering, 4(2), 245-248.

Dueck, G. (1993). New Optimization Heuristics: The Great Deluge Algorithm and the Record-to-Record Travel. Journal of Computational Physics, 104(1), 86-92.  

Eley, M. (2006). Ant algorithms for the exam timetabling problem Michael. International Conference on the Practice and Theory of Automated Timetabling, PP. 167-180.  

Glover, F. (1986). Future paths for integer programming and links to artificial intelligence. Computers & Operations research, 13(5), 533-549.

Hmer, A. & Mouhoub, M. (2014). A Multi-Phase Hybrid Metaheuristics Approach for the Exam Timetabling, 10th International Conference of the Practice and Theory of Automated Timetabling, PP. 26-29.

Hsu, C. & Chao, H. (2009). 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. Proceedings of the 2009 WRI Global Congress on Intelligent Systems, PP. 379-384.

Ikuo, O. & Kunihito, Y. & Yoshiyuki, S. (2004). Timetabling For Satisfying Professors’ Requirements and Student’s Desires using Genetic Algoritm, Memoirs of the Faculty of Engineering, Miyazaki University, PP. 313-318.

Juang, Y. & Kao, H. & Lin, S. (2007). An adaptive scheduling system with genetic algorithms for arranging employee training programs, Elsevier Expert Systems with Applications, 33(3), 642-651.

Kahar, M. & Kendall, G. (2015). A great deluge algorithm for a real-world examination timetabling problem. Journal of The Operational Research Society, 66(1), 116-133.

Kirkpatrick, S., Gelatt, C.D., Vecchi, M.P. (1983). Optimization by Simulated Annealing Science. Journal of Computational Physics, 220(4598), 671-680.

Liu, J. & Yu, X. & Wang, Z. (2009). Self-Fertilization Based Genetic Algoritm for University TimeTabling Problem. Proceedings of the first ACM/SIGEVO Summit on Genetic and Evolutionary Computation, PP. 1001-1004.

Marc, R. & Lewis, R. (2006). Metaheuristics for University Course Timetabling. A thesis submitted in partial fulfilment of the requirements of Napier University for the degree of Doctor of Philosophy.

Müller, T. & Rudová, H. & Murray, K. (2010). Complex university course timetabling. Journal of Scheduling, 14(2), 187-207.

Pilla, N. (2014). A study of the practical and tutorial scheduling problem. 10 th International Conference of the Practice and Theory of Automated Timetabling, PTAT, PP. 26-29.

Rayward Smith, V.J., Osman, I.H., Reeves, G.D., Smith, G.D. (1996). Modern Huristics Search Methods. John Wiley& Sons Ltd.

Wahid, J. & Mohd Hussin, N. (2014). Harmony search algorithm for curriculum- based course timetabling problem. International Journal of Soft Computing and Software Engineering, 3(3), 365-371.