Fuzzy analytic hierarchy process (FAHP) has been extensively applied to multi-criteria decision making (MCDM). However, the computational burden resulting from the calculation of fuzzy eigenvalue and eigenvector is heavy. As a result, a FAHP problem is usually solved using approximation techniques such as fuzzy geometric mean (FGM) and fuzzy extent analysis (FEA) instead of exact methods. Therefore, the FAHP results are subject to considerable inaccuracy. To solve this problem, in this study, a FAHP method based on the combination of α-cut operations (ACO), center-of-gravity (COG) defuzzification and defuzzification convergence mechanism (DCM) is proposed. First, ACO is applied to derive the near-exact fuzzy maximal eigenvalue and fuzzy weights. Subsequently, the α cuts of the fuzzy maximal eigenvalue and fuzzy weights are interpolated to generate samples that are uniformly distributed along the x-axis so that COG can be correctly applied to defuzzify the fuzzy maximal eigenvalue and fuzzy weights. To accelerate the computation process, DCM is applied to terminate the enumeration process if the defuzzified values of fuzzy weights have converged. The ACO–COG–DCM method has been applied to a real case to illustrate its applicability. In addition, a simulation study was also conducted to perform a parametric analysis. According to the experimental results, the proposed ACO–COG–DCM method improved the accuracy of estimating fuzzy weights by up to 56%. Furthermore, the experimental results also showed that the inaccuracy of estimating fuzzy weights was mostly owing to the deficiency of the FAHP method rather than the inconsistency of fuzzy pairwise comparison results.

模糊层次分析法（FAHP）已广泛应用于多准则决策（MCDM）。然而，由于模糊特征值和特征向量的计算而产生的计算负担很重。结果，通常使用诸如模糊几何均值（FGM）和模糊程度分析（FEA）的近似技术代替精确方法来解决FAHP问题。因此，FAHP结果存在很大的误差。为了解决这个问题，本研究提出了一种结合α- cut操作（ACO），重心（COG）去模糊和去模糊收敛机制（DCM）的FAHP方法。首先，应用ACO推导近似精确的模糊最大特征值和模糊权重。随后，α对模糊最大特征值和模糊权重的割线进行插值以生成沿x均匀分布的样本轴，以便可以正确应用COG对模糊最大特征值和模糊权重进行模糊化处理。为了加速计算过程，如果模糊权重的去模糊值收敛，则将DCM应用于终止枚举过程。ACO-COG-DCM方法已应用于实际案例，以说明其适用性。此外，还进行了仿真研究以执行参数分析。根据实验结果，提出的ACO – COG – DCM方法将估计模糊权重的准确性提高了56％。此外，实验结果还表明，估计模糊权重的准确性主要是由于FAHP方法的不足，而不是模糊成对比较结果的不一致。