Performance analysis of graph heuristics and selected trajectory metaheuristics on examination timetable problem - Mandal, Ashis Kumar; Mohd Nizam Mohmad, Kahar (Institute of Advanced Engineering and Science, 2020)
Examination timetabling problem is hard to solve due to its NP-hard nature, with a large number of constraints having to be accommodated. To deal with the problem effectually, frequently heuristics are used for constructing a feasible examination timetable, while meta-heuristics are applied for improving the solution quality. In this paper, we present the combination of graph heuristics and major trajectory metaheuristics for addressing both capacitated and un-capacitated examination timetabling problem. For constructing the feasible solution, six graph heuristics are used. They are largest degree (LD), largest weighted degree (LWD), largest enrolment degree (LE), and three hybrid heuristics with saturation degree (SD) such as SD-LD, SD-LE, and SD-LWD. Five trajectory algorithms comprising of tabu search (TS), simulated annealing (SA), late acceptance hill-climbing (LAHC), great deluge algorithm (GDA), and variable neighborhood search (VNS) are employed for improving the solution quality. Experiments have been tested on several instances of un-capacitated and capacitated benchmark datasets, which are Toronto and ITC2007 datasets, respectively. Experimental results indicate that hybrid graph heuristics produce the best initial solutions across both these benchmark problems. The study also reveals that, during improvement, GDA, SA, and LAHC can produce better quality solutions compared to TS and VNS for solving both benchmark datasets. Moreover, the performance of our method is comparable with other approaches in the scientific literature.