A robust simulated annealing based examination timetabling system
Computers and Operations Research
A Survey of Automated Timetabling
Artificial Intelligence Review
Scheduling, Timetabling and Rostering - A Special Relationship?
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
Parallelization of the scatter search for the p-median problem
Parallel Computing - Special issue: Parallel computing in logistics
Scatter Search: Methodology and Implementations in C
Scatter Search: Methodology and Implementations in C
Parallel Metaheuristics: A New Class of Algorithms
Parallel Metaheuristics: A New Class of Algorithms
A multi-objective evolutionary algorithm for examination timetabling
Journal of Scheduling
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
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
Parallel scatter search algorithm for the flow shop sequencing problem
PPAM'07 Proceedings of the 7th international conference on Parallel processing and applied mathematics
Scatter search technique for exam timetabling
Applied Intelligence
PATAT'04 Proceedings of the 5th international conference on Practice and Theory of Automated Timetabling
A hybrid multi-objective evolutionary algorithm for the uncapacitated exam proximity problem
PATAT'04 Proceedings of the 5th international conference on Practice and Theory of Automated Timetabling
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University exam timetabling refers to scheduling exams into predefined days, time periods and rooms, given a set of constraints. Exam timetabling is a computationally intractable optimization problem, which requires heuristic techniques for producing adequate solutions within reasonable execution time. For large numbers of exams and students, sequential algorithms are likely to be time consuming. This paper presents parallel scatter search meta-heuristic algorithms for producing good sub-optimal exam timetables in a reasonable time. Scatter search is a population-based approach that generates solutions over a number of iterations and aims to combine diversification and search intensification. The authors propose parallel scatter search algorithms that are based on distributing the population of candidate solutions over a number of processors in a PC cluster environment. The main components of scatter search are computed in parallel and efficient communication techniques are employed. Empirical results show that the proposed parallel scatter search algorithms yield good speed-up. Also, they show that parallel scatter search algorithms improve solution quality because they explore larger parts of the search space within reasonable time, in contrast with the sequential algorithm.