Selection of cost drivers in Activity Based Costing with fuzzy genetic algorithms

Abstract In view of the limitations of traditional cost systems, Activity Based Costing takes the line that it is appropriate to identify the source of cost rather than concentrating solely on symptoms . This system implies a need to identify activities and choose cost drivers . The chief difficulty recognized in the literature is precisely how to select drivers , owing to the large number of activities and the uncertainty inherent in the range of variables that are involved in this process. In such a situation it becomes necessary, on the one hand, to set up mechanisms allowing complex problems to be handled, the line being taken in this paper that for such purposes genetic algorithms are of use . On the other hand , it is important to enable their application jointly with tools that permit uncertain information to be dealt with, which in its tum justifies interest in the Theory of Fuzzy Sets that can operate with this type of information. Hence, this paper presents a Fuzzy Genetic Algorithm as a mechanism facilitating the process of selection of cost drivers in ABC systems in which it is necessary to aggregate or lump together activities