Key words: evolutionary algorithms, grouping problem

Group Technology (GT) is philosophy, which aims to decompose a manufacturing system into autonomous subsystems (groups). The objective is to aggregating similar parts into families and dissimilar machines into cells such that inter-cell movement of parts is minimized. From the works by E. Falkenauer it appears that a standard scheme and elements of an evolutionary algorithm are not suitable for the problem of grouping the elements. These observations are confirmed by the other researches. As a remedy E. Falkenauer proposed a new encoding scheme and genetic operators adapted to the grouping problem, yielding Grouping Genetic Algorithm (GGA). In this paper we investigate the use of not specialized evolutionary strategy for manufacturing cell design. We used (1,λ)-ES, where 30 children are generated from one parent by means of the simple mutations; the crossover is not applied. The best of the descendants becomes the new parent solution. The experiments shown a great usefulness of the evolutionary strategy for the cell design problem. The results confirmed once more the power of the evolutionary algorithms, which consists in ability to generate very good solutions without going into the structure of the problem.