Using simulated annealing and genetic algorithms to solve staff scheduling problems

In this paper we apply the heuristic search optimisation methods of simulated annealing and genetic algorithms to the problem of scheduling staff with different skill levels. Optimal solutions to staff scheduling problems are generally very difficult to achieve because of the large number of alternative solutions. Descent algorithms get trapped in local optima, which can be far removed from the global optimum. Our results show that both the approaches of simulated annealing and genetic algorithms can produce optimal or near-optimal solutions in a relatively short time for a nurse scheduling problem.