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AI Summary & Key Takeaways
AI Insight⚡ TL;DR(Too Long; Didn't Read)
We also show that the principal eigenvector solution is essential for deriving the scale of priorities from the fundamental scale of judgments given in the matrix.
📌 3 Key Takeaways
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Objective & Hypothesis:In this paper we give a skeletal outline of the foundations of the Analytic Hierarchy Process and some of its highlights.
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Key Findings & Data:In this paper we give a skeletal outline of the foundations of the Analytic Hierarchy Process and some of its highlights.
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Conclusion & Impact:We also show that the principal eigenvector solution is essential for deriving the scale of priorities from the fundamental scale of judgments given in the matrix.
🔬Design:Research Article
📍Scope:Comprehensive Sample
⚙️Variables:Domain Parameters
Abstract
In this paper we give a skeletal outline of the foundations of the Analytic Hierarchy Process and some of its highlights. We also show that the principal eigenvector solution is essential for deriving the scale of priorities from the fundamental scale of judgments given in the matrix.
Özet
Bu makalede Analitik Hiyerarşi Sürecinin temellerinin bir taslağı ile bu temellerin bazılarının detayı verilmektedir. Ayrıca, öncelikli özvektör çözümünün, matrislerle verilen yargıların temel ölçeği ile elde edilen önceliklerin değerlerini elde etmek için çok önemli olduğu gösterilmiştir.
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Cite This Article
YETİM, S. (2022). Some Mathematical Concepts of the Analytic Hiyerarchy Process. Gazi Üniversitesi Kastamonu Eğitim Dergisi, Cilt: 12 - Sayı: 2, 457–468. https://ajindex.com/articles/523209