Effective genetic operators of cooperative genetic algorithm for nurse scheduling

This paper proposes effective genetic operators for cooperative genetic algorithm (GA) to solve a nurse scheduling problem. A clinical director of a medical department makes a duty schedule of all nurses of the department every month. Such the scheduling is very complex task. It takes one or two weeks to create the nurse schedule even by a veteran director. In conventional ways using the cooperative GA, a crossover operator is only employed for the optimization, because it does not lose consistency between chromosomes. We propose a mutation operator and a virus operator for the cooperative GA, which does not lose consistency of the nurse schedule. The cooperative GA with these new operators has brought a surprisingly good result, it has never been brought by the conventional algorithm.