Solving University Course Timetabling Problem Using Parallel Genetic Algorithm

Abstract

Scheduling is one of the problems which so many researches have been conducted on it over the years. The university course timetabling problem (UCTP) which is an NP-Hard problem is a type of scheduling problem. The allocation of whole of events in timeslots and rooms performs by the university course timetabling process considering the list of hard and soft constraints presented in one semester, so that no conflict is created in such allocations. In general, it means assigning predefined courses to certain rooms and timeslots under specific constraints. In this paper we establish a hybrid algorithm based on Parallel Genetic Algorithm and Local Search to solve course timetabling problem (PGALS). This combines a direct representation of the timetable with heuristic crossover operators to ensure that the most fundamental constraints are never violated. We see how the algorithm is guaranteed to always produce a feasible solution by hard coding constraints which must not be broken. The proposed algorithm has been applied and evaluated against the latest methodologies in the literature with respect to standard benchmark problems. We demonstrate that the proposed algorithm produces some of the best-known results when tested on BenPaechter competition datasets.