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Estimation of priority weights based on a resampling technique and a ranking method in analytic hierarchy process
Author(s) -
Basak Indrani
Publication year - 2019
Publication title -
journal of multi‐criteria decision analysis
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.462
H-Index - 47
eISSN - 1099-1360
pISSN - 1057-9214
DOI - 10.1002/mcda.1664
Subject(s) - pairwise comparison , resampling , consistency (knowledge bases) , ranking (information retrieval) , rank (graph theory) , analytic hierarchy process , mathematics , hierarchy , matrix (chemical analysis) , set (abstract data type) , process (computing) , data mining , computer science , statistics , mathematical optimization , artificial intelligence , discrete mathematics , combinatorics , operations research , economics , market economy , programming language , materials science , composite material , operating system
Individual judgments are sought in order to elicit values of the entries of pairwise comparison matrices in analytic hierarchy process. Some of these matrices are more consistent than others. But throwing out inconsistent matrices reduces the number of matrices. In this article, we propose a resampling technique to generate sets of pairwise comparison matrices, which pass the consistency check. The advantage of the resampling technique is that one can generate as many sets of pairwise comparison matrices as needed to select the ones that satisfy the consistency requirement. Based on these selected matrices, the priority weights of the alternatives are then estimated. We propose rank‐based statistical procedures to check the significance in the difference between estimated priorities of the alternatives to establish their most significant rank order.