A survey of practical applications of examination timetabling algorithms
Operations Research
Constraint satisfaction in logic programming
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Scheduling examinations to reduce second-order conflicts
Computers and Operations Research
A tabu search heuristic for the vehicle routing problem
Management Science
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A robust simulated annealing based examination timetabling system
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Genetic Algorithms in Search, Optimization and Machine Learning
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A Survey of Automated Timetabling
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Improving Evolutionary Timetabling with Delta Evaluation and Directed Mutation
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Selected papers from the First International Conference on Practice and Theory of Automated Timetabling
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Selected papers from the First International Conference on Practice and Theory of Automated Timetabling
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Selected papers from the First International Conference on Practice and Theory of Automated Timetabling
Examination Timetabling in British Universities: A Survey
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General Cooling Schedules for a Simulated Annealing Based Timetabling System
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Some Observations about GA-Based Exam Timetabling
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A Constraint-Based Approach for Examination Timetabling Using Local Repair Techniques
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An Examination Scheduling Model to Maximize Students' Study Time
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Specialised Recombinative Operators for Timetabling Problems
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Advances in evolutionary computing
Case-based heuristic selection for timetabling problems
Journal of Scheduling
Co-evolutionary algorithm approach to a university timetable system
CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
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Evolutionary Computation
Journal of Artificial Intelligence Research
The micro genetic algorithm 2: towards online adaptation in evolutionary multiobjective optimization
EMO'03 Proceedings of the 2nd international conference on Evolutionary multi-criterion optimization
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
Ant algorithms for the exam timetabling problem
PATAT'06 Proceedings of the 6th international conference on Practice and theory of automated timetabling VI
An experimental study on hyper-heuristics and exam timetabling
PATAT'06 Proceedings of the 6th international conference on Practice and theory of automated timetabling VI
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
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Fuzzy multiple heuristic orderings for examination timetabling
PATAT'04 Proceedings of the 5th international conference on Practice and Theory of Automated Timetabling
No free lunch theorems for optimization
IEEE Transactions on Evolutionary Computation
A multistage evolutionary algorithm for the timetable problem
IEEE Transactions on Evolutionary Computation
Microgenetic algorithms as generalized hill-climbing operators forGA optimization
IEEE Transactions on Evolutionary Computation
Proceedings of the 2009 International Conference on Hybrid Information Technology
Scatter search technique for exam timetabling
Applied Intelligence
Information Sciences: an International Journal
Parallel Scatter Search Algorithms for Exam Timetabling
International Journal of Applied Metaheuristic Computing
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This paper considers the scheduling of exams for a set of university courses. The solution to this exam timetabling problem involves the optimization of complete timetables such that there are as few occurrences of students having to take exams in consecutive periods as possible but at the same time minimizing the timetable length and satisfying hard constraints such as seating capacity and no overlapping exams. To solve such a multi-objective combinatorial optimization problem, this paper presents a multi-objective evolutionary algorithm that uses a variable-length chromosome representation and incorporates a micro-genetic algorithm and a hill-climber for local exploitation and a goal-based Pareto ranking scheme for assigning the relative strength of solutions. It also imports several features from the research on the graph coloring problem. The proposed algorithm is shown to be a more general exam timetabling problem solver in that it does not require any prior information of the timetable length to be effective. It is also tested against a few influential and recent optimization techniques and is found to be superior on four out of seven publicly available datasets.