Neighborhood Analysis: a Case Study on Curriculum-Based Course Timetabling

In this paper, we present an in-depth analysis of neighborhood relations for local search algorithms. Using a curriculum-based course timetabling problem as a case study, we investigate the search capability of four neighborhoods based on three evaluation criteria: percentage of improving neighbors, improvement strength and search steps. This analysis shows clear correlations of the search performance of a neighborhood with these criteria and provides useful insights on the very nature of the neighborhood. This study helps understand why a neighborhood performs better than another one and why and how some neighborhoods can be favorably combined to increase their search power. This study reduces the existing gap between reporting experimental assessments of local search-based algorithms and understanding their behaviors

Fuzzy Multiple Heuristic Ordering for Examination Timetabling

In this paper, we address the issue of ordering exams by simultaneously considering two separate heuristics using fuzzy methods. Combinations of two of the following three heuristic orderings are employed: largest degree, saturation degree and largest enrolment. The fuzzy weight of an exam is used to represent how difficult it is to schedule. The decreasingly ordered exams are sequentially chosen to be assigned to the last slot with least penalty cost value while the feasibility of the timetable is maintained throughout the process. Unscheduling and rescheduling exams is performed until all exams are scheduled. The proposed algorithm has been tested on 12 benchmark examination timetabling data sets and the results show that this approach can produce good quality solutions. Moreover, there is significant potential to extend the approach by including a larger range of heuristics

A Novel Similarity Measure for Heuristic Selection in Examination Timetabling

Metaheuristic approaches to examination timetabling problems are usually split up into two phases: initialisation phase in which a heuristic is employed to construct an initial solution and improvement phase which employs a metaheuristic. Different hybridisations of metaheuristics with sequential heuristics are known to lead to solutions of different quality. A Case Based Reasoning CBR methodology has been developed for selecting an appropriate hybridsation of Great Deluge metaheuristic with a sequential construction heuristic. In this paper we propose a new similarity measure between two timetabling problems that is based on fuzzy sets. The experiments were performed on a number of real-world problems and the results were also compared with other state-of-theart methods. The results obtained show the effectiveness of the developed CBR system