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Fuzzy Approach to Quantification of Technical and Schedule Uncertainty in DoD Weapon System Cost Estimates

机译:国防部武器系统成本估算中技术和进度不确定性量化的模糊方法

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Cost estimating, within DoD for acquisition of weapon systems, is a target of criticism. Quantification of uncertainty in cost estimates is a subject of concern at the highest levels of review. Due to time constraints associated with developing an estimate to accompany a procurement package through the acquisition process, the cost analysis community reverts to giving less than adequate attention to quantification of technical and schedule uncertainty. This research describes a methodology and provides an analysis for the quantification of technical and schedule uncertainty using two elements of fuzzy logic, linguistic variables and fuzzy membership sets. Application of the methodology presented is context specific, related to a particular fielded weapon system. Foundation for the methodology rests on a survey of technical and schedule uncertainty that requires input, in the form of opinions of percent ranges of uncertainty, for five categories; very low, low, medium, high and very high. Thirty professional personnel supporting the particular weapon system office completed all sections of the survey for each of the five linguistic categories for both technical and schedule uncertainty. Survey input is characterized using triangular and trapezoidal functions clearly depicting overlapping ranges of each category into classes of fuzzy membership sets. A rule base, in the form of a look-up table matrix, is presented for combining technical and schedule uncertainty. Methodology presented in the research is robust enough to accommodate either linguistic or quantitative input, process the input through a fuzzy algorithm and defuzzify output of the algorithm to a crisp solution. QuickBASIC computer programs are documented for insight into precisely how the degrees of membership for each fuzzy set are generated. Pattern search algorithms for specifying triangular and trapedzoidal parameters for each fuzzy membership set are presented.

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