Examination timetabling by computer
Computers and Operations Research
Fuzzy sets, uncertainty, and information
Fuzzy sets, uncertainty, and information
New methods to color the vertices of a graph
Communications of the ACM
Scheduling under Fuzziness
Artificial Intelligence: A Guide to Intelligent Systems
Artificial Intelligence: A Guide to Intelligent Systems
PATAT '00 Selected papers from the Third International Conference on Practice and Theory of Automated Timetabling III
Scheduling, Timetabling and Rostering - A Special Relationship?
Selected papers from the First International Conference on Practice and Theory of Automated Timetabling
A Memetic Algorithm for University Exam Timetabling
Selected papers from the First International Conference on Practice and Theory of Automated Timetabling
Examination Timetabling in British Universities: A Survey
Selected papers from the First International Conference on Practice and Theory of Automated Timetabling
Some Observations about GA-Based Exam Timetabling
PATAT '97 Selected papers from the Second International Conference on Practice and Theory of Automated Timetabling II
Solving Rostering Tasks as Constraint Optimization
PATAT '00 Selected papers from the Third International Conference on Practice and Theory of Automated Timetabling III
A Multicriteria Approach to Examination Timetabling
PATAT '00 Selected papers from the Third International Conference on Practice and Theory of Automated Timetabling III
Tabu Search Techniques for Examination Timetabling
PATAT '00 Selected papers from the Third International Conference on Practice and Theory of Automated Timetabling III
A Tabu-Search Hyperheuristic for Timetabling and Rostering
Journal of Heuristics
Case-based heuristic selection for timetabling problems
Journal of Scheduling
Initialization strategies and diversity in evolutionary timetabling
Evolutionary Computation
A Computational Approach to Approximate and Plausible Reasoning with Applications to Expert Systems
IEEE Transactions on Pattern Analysis and Machine Intelligence
A novel similarity measure for heuristic selection in examination timetabling
PATAT'04 Proceedings of the 5th international conference on Practice and Theory of Automated Timetabling
Fuzzy multiple heuristic orderings for examination timetabling
PATAT'04 Proceedings of the 5th international conference on Practice and Theory of Automated Timetabling
A multistage evolutionary algorithm for the timetable problem
IEEE Transactions on Evolutionary Computation
Case-based heuristic selection for timetabling problems
Journal of Scheduling
Computers and Operations Research
A perspective on bridging the gap between theory and practice in university timetabling
PATAT'06 Proceedings of the 6th international conference on Practice and theory of automated timetabling VI
A novel fuzzy approach to evaluate the quality of examination timetabling
PATAT'06 Proceedings of the 6th international conference on Practice and theory of automated timetabling VI
Fuzzy multiple heuristic orderings for examination timetabling
PATAT'04 Proceedings of the 5th international conference on Practice and Theory of Automated Timetabling
A hybrid particle swarm optimization based algorithm for high school timetabling problems
Applied Soft Computing
Hi-index | 0.00 |
The aim of this paper is to consider flexible constraint satisfaction in timetabling problems. The research is carried out in the context of university examination timetabling. Examination timetabling is subject to two types of constraints: hard constraints that must not be violated, and soft constraints that often have to be violated to some extent. Usually, an objective function is introduced to measure the satisfaction of soft constraints in the solution by summing up the number of students involved in the violation of the constraint. In existing timetabling models the binary logic strategy is employed to handle the satisfaction of the constraints, i.e. a constraint is either satisfied or not. However, there are some constraints that are difficult to evaluate using the binary logic: for example, the constraint that large exams should be scheduled early in the timetable. Fuzzy IF–THEN rules are defined to derive the satisfaction degree of this constraint, where both the size of the exam and the time period that the exams are scheduled in are expressed using the linguistic descriptors Small, Medium and Large, and Early, Middle and Late, respectively. In a similar way, the constraint that students should have enough break between two exams is modelled. A number of memetic algorithms with different characteristics are developed where corresponding fitness functions aggregate the satisfaction degrees of both fuzzy constraints. The proposed approach is tested on real-world benchmark problems and the results obtained are discussed.