Genetic Algorithm for lecturing schedule optimization - David Kristiadi; Rudy Hartanto (IJCCS (Indonesian Journal of Computing and Cybernetics Systems), 2019)
Scheduling is a classic problem in lecturing. Rooms, lecturers, times and scheduling constraints must be managed well to get an optimal schedule. University of Boyolali (UBY) also encounter the same scheduling problems. The problem was tried to be solved by building a library based on Genetic Algorithm (GA). GA is a computation method which inspired by natural selection. The computation consists of some operators i.e. Tournament Selection, Uniform Crossover, Weak Parent Replacement and two mutation operators (Interchanging Mutation and Violated Directed Mutation (VDM)). The two mutation method are compared to find which better mutation operator. The library was planned to have a capability to define custom constraints (scheduling requirements that were not accommodated by the library) without core program modifications. The test results show that VDM is more promising for optimal solutions than Interchanging Mutation. In UBY cases, optimal solution (fitness value=1) is reached in 12 minutes 41 second with adding 6 new room and inactivated 2 constraint i.e. lecturing begins at 14.00 except for 3rd semester of science law study program with morning class and lecturing participants must not over classroom capacity.