High school weekly timetabling by evolutionary algorithms
Proceedings of the 1999 ACM symposium on Applied computing
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
A Hybrid Genetic Algorithm for Highly Constrained Timetabling Problems
Proceedings of the 6th International Conference on Genetic Algorithms
Experiments on Networks of Employee Timetabling Problems
PATAT '97 Selected papers from the Second International Conference on Practice and Theory of Automated Timetabling II
Specialised Recombinative Operators for Timetabling Problems
Selected Papers from AISB Workshop on Evolutionary Computing
A Hybrid Genetic Algorithm for School Timetabling
AI '02 Proceedings of the 15th Australian Joint Conference on Artificial Intelligence: Advances in Artificial Intelligence
A global repair operator for capacitated arc routing problem
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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Creating an employee schedule means taking into account many heavy constraints like employee contracts or minimal staffing levels on the one hand and many global, difficult to formalize constraints like aspects of fairness on the other hand. Optimisation is quite difficult especially when fix rostering schemata cannot be used, e.g. because of frequently varying staffing levels. In this paper we present how real-life employee scheduling problems can be solved by applying a Hybrid Genetic Algorithm that uses problem specific knowledge. First we briefly describe the given problem domain, then the idea and implementation of the Genetic Algorithm is presented. Finally we show some application results and the outlook.