Faculty Course Scheduling Optimization
Keywords:
course scheduling, optimization, linear programming, genetic algorithms, binary integer programming.Abstract
Faculty course scheduling optimization is the second of the three stages of the University Course Timetable Problem optimization. The optimization process was modelled using genetic algorithms, binary integer programming, and linear programming. There are four simple problems and four difficult problems that were used in the study. Linear programming had the highest total rating but infeasible because it produced fractional timetable values. Since the output of both genetic algorithms and binary integer programming were feasible and the total rating of binary integer programming was higher, it was considered as the best model. The binary integer programming model gives the optimal solution for as long as formulation of the needed functions and constraints is possible and the solver can process them. An alternative model is the genetic algorithms that are capable of giving feasible solutions even in very complicated scheduling conditions. The linear programming model is the basis of the correctness of the output provided by the other two models because its optimum value is usually higher than the other models.
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