Prioritizing risk events of a large hydroelectric project using fuzzy analytic hierarchy process

Abstract

The existence of hydroelectric plants along Amazon River tributaries is a solution to satisfy the energy demand in Brazil. However, these plants are subjected to multiple risk events because of the geographic and socioeconomic characteristics of this region. In helping to address these escalating challenges, this paper presents a framework that assesses the risk events of service packs relevant to the plant. This framework presents a transparent approach for prioritizing risk events in large projects. The weights of importance of risk events are estimated using the fuzzy analytic hierarchy process. Chang’s extent analysis method takes into consideration the vagueness and imprecision of subjective human judgments. The convergence of decisions is evaluated using two aggregation approaches, namely the maximum-minimum method based on an arithmetic mean and a geometric mean. The performances of the original and modified extent analysis methods are compared using group Euclidean distance and distance between weights metrics. The degree of similarity between the evaluation metrics is examined using Spearman’s rank correlation coefficient and average overlap approaches. Due to the inconsistency of the reported results, the final rankings of the aggregation approaches are determined using a new aggregated multiple criteria decision making method. The results indicate that the original extent analysis method using the maximum-minimum method (arithmetic mean) is the best aggregation method. A Santo Antonio hydroelectric plant in Brazil is used to demonstrate the application of the proposed framework.