Multiobjective fuzzy competence set expansion problem by multistage decision-based hybrid genetic algorithms

The problem of competence set expansion involves determining the optimal expansion path under the minimum cost and has been widely used for the problem of process planning. As we know, the conventional competence set model only considers the problems of the single objective and the static situations. In practice, however, the multiobjective and the dynamic situations usually occur in the optimal expansion path problems. Furthermore, due to the subjective judgment and the restriction of information, the degree of uncertainty should also be considered. In this paper, a multiobjective and multistage fuzzy competence set model is proposed to overcome the problems above simultaneously. In order to efficiently obtain the optimal expansion path, hybrid genetic algorithms (hGA) are employed here. In addition, a particular job-shop scheduling example is used to demonstrate the proposed method. On the basis of the numerical results, we can conclude that the proposed method can provide a sound fuzzy competence set model by considering the multiobjective and the multistage situations simultaneously.