Nurse Scheduling by Using Cooperative GA with Efficient Mutation and Mountain-Climbing Operators

This paper proposes an effective algorithm to solve a nurse scheduling problem by using genetic algorithm (GA). In hospitals, nurses are assigned to any section such as the internal medicine department or the pediatrics department. In generally, 15-30 nurses belong to a section. A clinical director of the department has to make a duty schedule of all nurses of the department every month. It is very complex task to create the nurse schedule. To improve this problem, we propose an effective algorithm to create and optimize the nurse schedule. Our algorithm is based on the cooperative GA. In conventional ways using the cooperative GA for the nurse scheduling, a crossover operator is only applied to optimize the schedule, because it keeps validity between chromosomes. As the first proposal of this paper, we apply a new mutation operator to the cooperative GA, which does not fail validity of the schedule. Although the cooperative GA with the crossover and mutation operators optimizes the nurse schedule better than the conventional GA, the optimization mostly stagnates at the middle game or the endgame. We consider that this stagnation is caused that the optimization is caught in a local minimum area of a solution space. To escape from the local minimum and to find better solution, we apply a mountain-climbing operator to the cooperative GA. Effectiveness of these two new operators are shown by practical experiments