New ARDLS model enhances stability in decision-making processes

A new optimization model called Anchored Regularized Direct Least Squares (ARDLS) has been introduced to address the instability of priority rankings in the Analytic Hierarchy Process (AHP). Traditional Direct Least Squares (DLS) methods can produce multiple solutions, especially with inconsistent data, leading to unreliable results. ARDLS integrates established prioritization operators like the Eigenvector method and Singular Value Decomposition as anchors within a regularization penalty to ensure a single, unique global minimum, thereby improving accuracy and stability. AI

RANK_REASON The item is a research paper detailing a new mathematical optimization model for a decision-making process. [lever_c_demoted from research: ic=1 ai=0.1]

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• Analytic Hierarchy Process
• Anchored Regularized Direct Least Squares
• ARDLS
• Cosine Maximization
• Kevin Kam Fung Yuen
• Pseudo-Inverse Gram Matrix
• singular value decomposition
• Weighted least squares

• paper
• other

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