Proposal of Super Pairwise Comparison Matrix

The key issue of FAHP application is how to derive fuzzy weights from fuzzy pairwise comparison matrix. The most of applications, however, were founding avoiding the use of sophisticated approaches such as fuzzy least squares method and using a simple extent analysis method to derive fuzzy weight from pairwise comparison matrix for the sake of simplicity. But the extent analysis method proves to be incorrect and may lead to a wrong decision result. So, this paper proposes a sound yet simple linear goal programming model to derive weights from pairwise fuzzy comparison matrix, which takes minimizing inconsistence degree of comparison matrix as objective and obtain a normalized weight vector finally. The proposed model is validated by an application to new product development scheme screening decision making.

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Improved Consistency Ratio for Pairwise Comparison Matrix in Analytic Hierarchy Processes

Asia Pacific Journal of Operational Research ◽

10.1142/s0217595916500202 ◽

2016 ◽

Vol 33 (03) ◽

pp. 1650020

Author(s):

L. N. Pradeep Kumar Rallabandi ◽

Ravindranath Vandrangi ◽

Subba Rao Rachakonda

Keyword(s):

Error Function ◽

Pairwise Comparison ◽

Large Set ◽

Simulation Experiments ◽

Analytic Hierarchy ◽

Chi Square ◽

Pairwise Comparison Matrix ◽

Comparison Matrix ◽

Hierarchy Process ◽

Original Information

The analytical hierarchy process (AHP) uses pairwise comparison matrix (PCM) to rank a known set of alternatives. Sometimes the comparisons made by the experts may be inconsistent which results in incorrect weights and rankings for the AHP. In this paper, a method is proposed which identifies inconsistent elements in a PCM and revises them iteratively until the inconsistency is reduced to an acceptable level. An error function similar to chi-square is used to identify the inconsistent elements which are revised with suitable values. The method is illustrated with some numerical examples mentioned in the literature and a comparative study of the results in terms of deviation from the PCM and preservation of original information is taken up. Monte Carlo simulation experiments over a large set of random matrices indicate that the proposed method converges for the moderately inconsistent matrices.

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Deriving weights from a pairwise comparison matrix over an alo-group

Soft Computing ◽

10.1007/s00500-011-0746-8 ◽

2011 ◽

Vol 16 (2) ◽

pp. 353-366 ◽

Cited By ~ 30

Author(s):

Bice Cavallo ◽

Livia D’Apuzzo

Keyword(s):

Pairwise Comparison ◽

Pairwise Comparison Matrix ◽

Comparison Matrix

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Utilization of AHP-MAUT Method to Determine the Country of Exhibition Abroad in Batik Hatta Boutique

Journal on Information Technology and Computer Engineering ◽

10.25077/jitce.5.02.52-56.2021 ◽

2021 ◽

Vol 5 (02) ◽

pp. 52-56

Author(s):

Saifur Rohman Cholil ◽

Tria Ardianita

Keyword(s):

Decision Making ◽

Rank Correlation ◽

Pairwise Comparison ◽

Hierarchical Level ◽

Spearman Rank Correlation ◽

Ahp Method ◽

Pairwise Comparison Matrix ◽

Spearman Rank ◽

Comparison Matrix ◽

A Value

This research was conducted with the aim of helping decide the destination country for overseas exhibitions at the Batik Hatta Boutique. By knowing all the data and information of a country, boutique owners can decide which country to visit in the batik exhibition. Because if you attend the cast in all countries, there will be overruns in costs. The methods used are AHP and MAUT. The AHP method is used as a weighting using a linguistic value scale. Weights are obtained from the pairwise comparison matrix between two elements of all elements that occur at the same hierarchical level. The MAUT method is used to determine the importance of each alternative for the ranking process. The results of this study indicate that Cambodia was chosen as the location to be visited for the batik exhibition. The results of the validation using the Spearman Rank correlation comparison obtained a value of 0.951 meaning that this method can be used as a decision making.

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