  This paper describes a Genetic Algorithms approach to a manpower-scheduling
problem arising at a major UK hospital. Although Genetic Algorithms have been
successfully used for similar problems in the past, they always had to overcome
the limitations of the classical Genetic Algorithms paradigm in handling the
conflict between objectives and constraints. The approach taken here is to use
an indirect coding based on permutations of the nurses, and a heuristic decoder
that builds schedules from these permutations. Computational experiments based
on 52 weeks of live data are used to evaluate three different decoders with
varying levels of intelligence, and four well-known crossover operators.
Results are further enhanced by introducing a hybrid crossover operator and by
making use of simple bounds to reduce the size of the solution space. The
results reveal that the proposed algorithm is able to find high quality
solutions and is both faster and more flexible than a recently published Tabu
Search approach.
