Effective Mutation Operator and Parallel Processing for Nurse Scheduling

This paper proposes an effective mutation operator and an effective parallel processing algorithm for cooperative genetic algorithm (CGA) to solve a nurse scheduling problem. The nurse scheduling is very complex task for a clinical director in a general hospital. Even veteran director needs one or two weeks to create the schedule. Besides, we extend the nurse schedule to permit the change of the schedule. This permission explosively increases computation time for the nurse scheduling. We propose the effective mutation operator for the CGA. This mutation operator does not lose consistency of the nurse schedule. Furthermore, we propose the parallel processing algorithm of the CGA. The parallel CGA always brings good results.