This paper proposes a heuristic method (Bayesian cosine maximization method (BCCM)) to rank the alternatives in the Analytic Hierarchy Process (AHP) synthesis, based on the multiplicative AHP model, which focuses on the revision of the pair-wise comparison matrices (PCMs) and derivation of the priority vectors from the PCMs in whole hierarchy, considering both the consistency of the PCMs and total consistency. An Eight-step algorithm for the AHP synthesis is developed to how to revise the PCMs in the uncertainty context and generate the final priority vector of the alternatives, which obtains more accurate estimates of the priority vectors and provides a global AHP framework based on the multiplicative AHP model. Finally, two numerical examples and corresponding comparison with several other methods are implemented to illustrate the application and efficiency of the proposed BCCM.

本文提出了一种启发式方法（贝叶斯余弦最大化方法（BCCM）），在基于乘法AHP模型的基础上，对层次分析法（AHP）进行了排序，重点是对成对比较矩阵（PCMs）的修订。 ），并从PCM整体层次结构中推导优先级向量，同时考虑PCM的一致性和总体一致性。开发了一种用于AHP综合的八步算法，以解决如何在不确定性上下文中修改PCM并生成备选方案的最终优先级向量的问题，从而获得更准确的优先级向量估计值，并提供基于乘法的全局AHP框架AHP模型。最后，