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

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Published Mar 1, 2023
Maryam Bagheri Fard Sharabiani Mohammad Reza Gholamian Gholamian Seyed Farid Ghannadpour

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.

How to Cite

Maryam Bagheri Fard Sharabiani, Gholamian, M. R. G., & Seyed Farid Ghannadpour. (2023). A NEW SOLUTION FOR INCOMPLETE AHP MODEL USING GOAL PROGRAMMING AND SIMILARITY FUNCTION. International Journal of the Analytic Hierarchy Process, 15(1). https://doi.org/10.13033/ijahp.v15i1.1003

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Keywords

Multi-Criteria Decision Making (MCDM), Analytic Hierarchy Process (AHP), Incomplete AHP, Goal Programming (GP), Similarity Function

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