Generating project risk membership functions based on experts’ estimates and alpha-cut variations

Abstract This paper presents an approach for generating project risk membership function (MFS) based on experts’ estimates and α-level variations using simulations. The proposed algorithm employs combination of computer and mathematics application in the area of risk assessment. The determination of appropriate MFS plays a substantial role in the performance of a fuzzy system. Most of the discussions in the previous literature on MFS generated, the assumptions that the risks are outlooked from similar perspective of the experts. However, this would be unlikely true in the real life when there is more than one expert from different background and experience. Proposed simulation method focuses on characteristics of MFS as well as the fuzzy numbers generation incorporating uncertainties in the experts’ inputs. Furthermore, results of set of fuzzy numbers of triangular MFS generated is presented in the fuzzy probability distribution and fuzzy cumulative distribution functions.

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