Handling Uncertain Information in Whole-life Costing: A Comparative Study

A number of recently developed algorithms to handle uncertain information in wholelife costing (WLC) are explained and validated in the context of two example applications. In the first example application, the proposed methodology is compared to the sensitivity analysis technique. The break-even point has been correctly identified in almost all cases. Furthermore, it has been shown how the fuzzy methodology employed may be seen as a generalised sensitivity approach to which has been added a measure of the precision with which input variables are known to the decisionmaker. In the second example application, the proposed methodology is compared to two probabilistic techniques: the confidence index method and the Monte Carlo simulation (MCS) technique. The proposed methodology correctly identified the most uncertain cost items and portrayed well the confidence in ranking. In addition, all predicted net present values were in close agreement with those obtained by the MCS technique. This again showed the robustness of various measures and concepts employed in the developed algorithms.