A New Linear Programming Method for Weights Generation and Group Decision Making in the Analytic Hierarchy Process
作者： Seyed Saeed Hosseinian , Hamidreza Navidi , Abas Hajfathaliha
DOI: 10.1007/S10726-009-9182-X
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摘要: This paper proposes a new linear programming method entitled by LP-GW-AHP for weights generation in analytic hierarchy process (AHP) which employs concepts from data envelopment analysis. We propose four specially constructed (LP) models are used to derive weight vector pair-wise comparison matrix or group of them. can use both interval and relative importance each decision maker LP-GW-AHP. In this method, solving only one LP model is enough local derivation matrices. Five numerical examples examined illustrate the potential applications method. show that not derived have slight differences than Saaty’s eigenvector but sometimes they better fitting performance index as well. compared with has been recently proposed AHP it becomes obvious provides weights. The simple additive weighting utilized aggregate without need normalize
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