LAUSR

Since university timetabling is commonly classified as a combinatorial optimisation problem, researchers tend to use optimisation approaches to reach the optimal timetable solution. Meta-heuristic algorithms have been presented as effective solutions as proven on their leverage over the last decade. Extensive literature studies have been published until today. However, a comprehensive systematic overview is missing. Therefore, this mapping study aimed to provide an organised view of the current state of the field and comprehensive awareness of the meta-heuristic approaches, by conducting meta-heuristic for solving university timetabling problems. In addition, the mapping study tried to highlight the intensity of publications over the last years, spotting the current trends and directions in the field of solving university timetabling problems, as well as having the work to provide guidance for future research by indicating the gaps and open questions to be fulfilled. Primary studies on mapping study that have been published in the last decade from 2009 until the first quarter of 2020, which consist of 131 publications, were selected as a benchmark for future research to solve university timetabling problems using meta-heuristic algorithms. The majority of the articles based on the publication type are hybrid methods (32%), in which the distribution of meta-heuristic algorithms the hybrid algorithms represent the higher application (31%). Likewise, the majority of the research is solution proposals (66%). The result of this study confirmed the efficiency and intensive application of the meta-heuristic algorithms in solving university timetabling problems, specifically the hybrid algorithms. A new trend of meta-heuristic algorithms such as grey wolf optimiser, cat swarm optimisation algorithm, Elitist self-adaptive step-size search and others with high expectations for reliable and satisfying results can be proposed to fill this gap.