Complex University Timetabling Using Iterative Forward Search Algorithm and Great Deluge Algorithm

Abstract

University timetabling is an issue that has received more attention in the field of operations research. Course scheduling is the process of arranging time slots and room for a class by paying attention to existing limitations. This problem is an NP-Hard problem, which means the computation time to find a solution increases exponentially with the size of the problem. Solutions to problems of this kind generally use a heuristic approach, which tries to find a sufficiently good (not necessarily optimal) solution in a reasonable time. We go through two stages in solving the timetabling problem. The first stage is to schedule all classes without breaking any predefined rules. The second stage optimizes the timetable generated in the first stage. This study attempts to solve the class timetabling problem issued in a competition called the 2019 International Timetabling Competition (ITC 2019). In the first stage, we use the Iterative Forward Search (IFS) algorithm to eliminate timetable candidates and to generate a schedule. In the second stage, we employ the Great Deluge algorithm with a hyper-heuristic approach to optimize the solution produced in the first stage. We have tested the method using 30 datasets by taking 1,000,000 iterations on each dataset. The result is an application that does schedule elimination and uses the IFS algorithm to produce a schedule that does not violate any of the hard constraints on 30 ITC 2019 datasets. The implementation of the Great Deluge algorithm optimizes existing schedules with an average penalty reduction of 42%.

The performance analysis of hyper-heuristics algorithms over examination timetabling problems

“…The GD algorithm is a local search procedure that has been widely used in research to solve exam timetabling problems at universities [11], [12]. The TS algorithm is another local search metaheuristic that is commonly used to solve combinatorial optimization problems [13], [14].…”

A Solving Route Optimization of Airplane Travel Problem Use Artificial Bee Colony Algorithm

“…TSP is also one of the most difficult combinatorial optimization problems. The TSP problem is included in the NP-Complete problem [10]- [12] where the running time increases exponentially with the increase in the size of problem [13]. Therefore, the exact algorithm's ability to solve this problem depends on the size of the problem.…”