The analytic hierarchy process (AHP) is a structured technique used to analyze complex decision-making situations such as resource allocation, benchmarking, and quality management. In the weight valuation step of using AHP to select the best design, pairwise comparison matrices are used to calculate the local priorities for designs that have contentious and unresolved criticisms. In this study, we propose a Bayesian approach using a Dirichlet-multinomial model to estimate local priorities during weight valuation. Experts are only asked to select the best design with respect to predetermined criterion. Subsequently, local priorities are estimated without pairwise comparison matrices. To improve the efficiency of the AHP, we propose two expert allocation policies (AHP-KG and AHP-AKG) based on the ranking and selection procedures. Our numerical results show that the proposed AHP-KG and AHP-AKG policies outperform pure exploration and proportional allocation policies.

中文翻译：

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一种层次分析过程中权重估值的模拟优化方法


层次分析过程 （AHP） 是一种结构化技术，用于分析复杂的决策情况，例如资源分配、基准测试和质量管理。在使用 AHP 选择最佳设计的权重评估步骤中，成对比较矩阵用于计算具有争议和未解决的批评的设计的局部优先级。在这项研究中，我们提出了一种贝叶斯方法，使用狄利克雷多项式模型来估计权重评估过程中的局部优先级。仅要求专家根据预定标准选择最佳设计。随后，在没有成对比较矩阵的情况下估计局部优先级。为了提高 AHP 的效率，我们根据排名和选择程序提出了两种专家分配策略（AHP-KG 和 AHP-AKG）。我们的数值结果表明，拟议的 AHP-KG 和 AHP-AKG 政策优于纯勘探和比例分配政策。