A NEW SOLUTION FOR INCOMPLETE AHP MODEL USING GOAL PROGRAMMING AND SIMILARITY FUNCTION

Maryam Bagheri, Fard Sharabiani, M. Gholamian
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引用次数: 0

Abstract

The pairwise comparison matrix (PCM) is a crucial element of the Analytic Hierarchy Process (AHP). In many cases, the PCM is incomplete and this complicates the decision-making process. Hence, the present study offers a novel approach for dealing with incomplete information in group decision-making. We present a new model of incomplete AHP using goal programming (GP) and the similarity function. The minimization of this similarity function reduces errors in decision-making. The proposed model will be able to estimate the unknown elements in the pairwise comparison matrix and calculate the weight vectors obtained from the matrices. Several examples are implemented to elaborate on the estimation of unknown elements and weight vectors in the proposed model. The results show that the unknown elements have an acceptable value with an appropriate consistency rate.
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用目标规划和相似函数求解不完全ahp模型
两两比较矩阵(PCM)是层次分析法(AHP)的重要组成部分。在许多情况下,PCM是不完整的,这使决策过程复杂化。因此,本研究为处理群体决策中的不完全信息提供了一种新的方法。利用目标规划和相似函数,提出了一种新的不完全层次分析法模型。这种相似性函数的最小化减少了决策中的错误。该模型将能够估计两两比较矩阵中的未知元素,并计算从矩阵中得到的权重向量。通过实例详细说明了该模型中未知元素和权向量的估计。结果表明,未知元素具有一个可接受的值,具有适当的一致性。
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来源期刊
International Journal of the Analytic Hierarchy Process
International Journal of the Analytic Hierarchy Process Decision Sciences-Decision Sciences (all)
CiteScore
2.30
自引率
0.00%
发文量
22
审稿时长
12 weeks
期刊介绍: IJAHP is a scholarly journal that publishes papers about research and applications of the Analytic Hierarchy Process(AHP) and Analytic Network Process(ANP), theories of measurement that can handle tangibles and intangibles; these methods are often applied in multicriteria decision making, prioritization, ranking and resource allocation, especially when groups of people are involved. The journal encourages research papers in both theory and applications. Empirical investigations, comparisons and exemplary real-world applications in diverse areas are particularly welcome.
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