Composite indicator development using utility function and fuzzy theory

Construction companies use composite indicators (CIs) to evaluate their overall project performance. However, the conventional methodology of CIs development causes indiscrimination, relative calibration, and redundancy. To address these problems, we propose a novel methodology that uses fuzzy theories. The proposed methodology includes a utility function for normalizing, a fuzzy measure for weighting, and a fuzzy integral for aggregating. We conducted a case study to assess the quality of the proposed methodology versus the alternative methodologies on 25 real projects of a construction company. The result showed that the measurement reliability of the proposed normalization method (1.96) is greater than that of the two different normalization methods (10.44 and 2.8, respectively). In addition, the measurement accuracy of the proposed aggregation method is greater than those of the four different aggregation methods. Therefore, our proposed methodology can more consistently and accurately help evaluate the overall project performance or success.