Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
A Memetic Approach to the Nurse Rostering Problem
Applied Intelligence
The State of the Art of Nurse Rostering
Journal of Scheduling
Cyclic preference scheduling of nurses using a Lagrangian-based heuristic
Journal of Scheduling
Memetic algorithms for nurse rostering
ISCIS'05 Proceedings of the 20th international conference on Computer and Information Sciences
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This paper proposes an effective mutation operator for Cooperative Genetic Algorithm (CGA) to be applied to a practical Nurse Scheduling Problem (NSP). NSP is a complex combinatorial optimizing problem for which many requirements must be considered. The changes of the shift schedule yields various problems, for example, a drop in the nursing level. The author describes a technique of the reoptimization of the nurse schedule in response to a change. CGA well suits local search, but its failure to handle global search leads to inferior solutions. CGA is superior in ability for local search by means of its crossover operator, but often stagnates at the global search. To solve this problem, a mutation operator activated is proposed depending on the optimization speed. This mutation yields small changes in the population depending on the optimization speed. Then the population is able to escape from a local minimum area by means of the mutation. However, this mutation operator is composed of two well-defined parameters. This means that users have to consider the value of the parameters carefully. To solve this problem, a periodic mutation operator is proposed which has only one parameter to define itself. This simplified mutation operator is effective over a wide range of the parameter value.