Comprehensive Decision-Making System Design and the New Educational Planning Scheduling Using Bees Algorithm Approach (Case Study: Payam-e Noor University, Lamard)

Document Type : Original Article

Authors

Abstract

Abstract: In this study, a single-objective model is presented for academic courses schedule problem. The purpose of this problem is to make appropriate and acceptable timetable for university courses taking into account a set of constraints and preferences of professors, students and universities according to the educational environment in Iran. In schedule problems, constraints are divided into two categories: hard and soft, hard constraints must be met, and ensure the feasibility of a solution, and soft constraints that represent utility and preferences of a problem, they are considered for better quality of a schedule. Given that an unsolvable problem is associated with computational complexity, meta-heuristic algorithms and bees algorithm are used to solve the models. To speed up the application, the initial population is produced in a way that a lot of hard constraints are saturated. In order to test the mathematical model and the app in question, the Lamerd PNU data is used. Solving model using bees method are derived from two important functions of dance, to escape from local optimum and improve the efficiency of the problem, and finally reach an acceptable solution, the results show that the accuracy of the mathematical model is 94 per cent, and c is better than classic status and general condition.

Keywords


Abdullah. S. & Turabieh. H. (2012). On the use of multi neighborhood structures within a Tabu-based memetic approach to university timetabling problems. Information Sciences,Malaysia, 146-168.
Aladag, C. H.; Hocaoglu, G. & Basaran, M. A. (2009). The effect of neighborhood structures on tabu search algorithm in solving course timetabling problem. Expert Systems with Applications, 12349–12356.
Aladag, C. H. & Hocaoglu, G. (2007). A tabu search algorithm to solve a course timetabling problem. Hacettepe Journal of Mathematics and Statistics, 53- 64.
Burke, E. K. & Petrovic, S. (2002). Recent research directions in automated timetabling. European Journal of Operational Research, 266–280.
Gunawan, A.; Ng, K. M. & Poh, K. L. (2012). A hybridized Lagrangian relaxation and simulated annealing method for the course timetabling problem. Computers & Operations Research, Singapore, 3074–3088.
Head, C. & Shaban, S. (2007). A heuristic approach to simultaneous course/student timetabling. Computers & Operations Research, 919–933.
Pham, D. T.; & Koç, E. (2010). Design of a two-dimensional recursive filter using the bee's algorithm. International Journal of Automation and Computing, 7 (3), 399-402.
Qaurooni. D. & Akbarzadeh-T. M-R. (2013). Course timetabling using evolutionary operators. Applied Soft Computing, 2504–2514.
Sabar, N. R.; Ayob, M.; Kendall, G. & Qu, R. (2012). A honey-bee mating optimization algorithm for educational timetabling problems. European Journal of Operational Research, 533-543.
Tereshko, V. & Loengarov, A. (2005). Collective decision making in honey-bee foraging dynamics. Computing and Information Systems, 9 (3), 1.
Wren, A. (1996). Scheduling, timetabling and rostering – A special relationship? In: Burke and Ross, 46–75.