Evolutionary algorithms for nurse scheduling problem

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The nurse scheduling problem (NSPs) represents a difficult class of multi-objective optimisation problems consisting of a number of interfering objectives between the hospitals and individual nurses. The objective of this research is to investigate difficulties that occur during the solution of NSP using evolutionary algorithms, in particular genetic algorithms (GA). As the solution method a population-less cooperative genetic algorithm (CGA) is taken into consideration. Because contrary to competitive GAs, we have to simultaneously deal with the optimization of the fitness of the individual nurses and also optimization of the entire schedule as the final solution to the problem in hand. To confirm the search ability of CGA, first a simplified version of NSP is examined. Later we report a more complex and useful version of the problem. We also compare CGA with another multi-agent evolutionary algorithm using pheromone style communication of real ants. Finally, we report the results of computer simulations acquired throughout the experiments.

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