| Abstract: | In this paper, we address a cost-drivers optimization (CDO) problem in which two separate but interrelated decisions (i.e. the number of cost drivers needed and which cost drivers to use) are considered. It is desirable to have (1) an optimal selection of cost drivers in order to provide better indication of product costs and (2) an optimal number of cost drivers in order to avoid excessive control costs and to minimize information costs associated with data collection, storage and processing. The objective of the CDO problem is to balance savings in information costs with loss of accuracy. We propose an heuristic procedure based on genetic algorithms as an alternative with the potential to address more generalized objective functions. Genetic algorithms represent an innovative and promising heuristic approach which does produce results superior to published alternatives. The development and implementation of the algorithm is supported with the literature review and comparative analysis. We also comment on the complexity and experimental design issues for addressing large and practical problems. |
