Staff Scheduling by a Genetic Algorithm

This paper describes a Genetic Algorithms approach to a manpower-scheduling problem arising at a Petrochemical Company. 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 personnel’s, 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. 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 Taboo Search approach