A simulation optimization approach for weight valuation in analytic hierarchy process
提出一种贝叶斯方法估计层次分析法中的局部优先级，并设计两种专家分配策略（AHP-KG和AHP-AKG）以提高权重评估效率，数值实验表明新策略优于纯探索和比例分配策略。
• A novel way of weight valuation for AHP is proposed. • A Bayesian approach is proposed to estimate the value of local priorities. • Two expert allocation policies are suggested for AHP. • The proposed policies enhance the efficiency of AHP. 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.