Evolutionary Algorithms for Nurse Scheduling Problem

Nurse scheduling problem (NSPs) represents a diicult class of Multi-objective optimization problems consisting of a number of interfering objectives between the hospitals and individual nurses. The objective of this research is to investigate diiculties that occur during solution of NSP using Evolutionary Algorithms, in particular Genetic Algorithms (GA). As the solution method a population-less Cooperative Genetic Algorithms (CGA) is taken into consideration. Because contrary to competitive GAs, we have to simultaneously deal with the optimization of the tness of the individual nurses and also optimization of the entire schedule as the nal solution to the problem in hand. To con-rm the search ability of CGA, rst a simpliied version of NSP is examined. Later we will report a more complex and useful version of the problem. We will also compare CGA with another multi-agent evolutionary algorithm using pheromone style communication of real ants. Finally , we will report the results of computer simulations acquired throughout the experiments.