Abstract
The course timetabling problem is not a trivial task as it is an NP-hard and NP-complete problem and many solutions have been proposed due to its high complexity search landscape. In essence, the nature of the course timetabling problem is to assign a lecturer-course entity to existing teaching venue and timeslot in an academic institution. In this article, the authors propose a Genetic Algorithm-Neighborhood Search (GANS) to construct a feasible timetable for courses offered by a department in the faculty of a local university in Malaysia. The framework of the solution is as follow: The feasible timetable is first constructed by Genetic Algorithm, which includes are pair operator which attempts to repair infeasible timetables. Upon feasibility, the second phase exploits the initial feasible solution using three neighborhood structures to search for an improved solution and global optimum. The experimental results demonstrate the efficiency and effectiveness of the various neighborhood structures in exploiting the feasible solutions to yield the global optimum.
Highlights
Problem BackgroundThe notion of manually generating a workable timetable within the context of a higher-learning institution is indefinitely a daunting task and the complexity increases when there are various unique hard constraints that must be satisfied in a feasible solution
This paper presents a hybrid Genetic Algorithm Neighborhood Search which integrates domain-specific exploitative
This section describes the results obtained from the proposed Genetic Algorithm-Neighborhood Search (GANS)
Summary
Problem BackgroundThe notion of manually generating a workable timetable within the context of a higher-learning institution is indefinitely a daunting task and the complexity increases when there are various unique hard constraints that must be satisfied in a feasible solution. This paper presents a hybrid Genetic Algorithm Neighborhood Search which integrates domain-specific exploitative Properties of the Neighborhood Search into Genetic Algorithm to solve the CTP adopted from a real world example from a faculty in Universiti Teknologi Malaysia.
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