FuzzyTOPSISVector: Implementation of Fuzzy TOPSIS Method for Multi-Criteria...

In FuzzyMCDM: Multi-Criteria Decision Making Methods for Fuzzy Data

Description

The FuzzyTOPSISVector function implements the Fuzzy Technique for Order of Preference by Similarity to Ideal Solution (Fuzzy TOPSIS) Method with the vector normalization procedure.

Usage

FuzzyTOPSISVector(decision, weights, cb)

Arguments

decision	The decision matrix (m x (n*3)) with the values of the m alternatives, for the n criteria, and multiplied by 3 since they are triangular fuzzy numbers.
weights	A vector of length n*3, containing the fuzzy weights for the criteria.
cb	A vector of length n. Each component is either cb(i)='max' if the i-th criterion is benefit or cb(i)='min' if the i-th criterion is a cost.

Value

FuzzyTOPSISVector returns a data frame which contains the score of the R index and the ranking of the alternatives.

References

Garcia-Cascales, M. S.; Lamata, M. T. and Sanchez-Lozano, J. M. Evaluation of photovoltaic cells in a multi-criteria decision making process. Annals of Operations Research, 199(1), 373-391, 2012.

Examples

 d <- matrix(c(0.68,0.4,0.6,0.2,0.4,1.44,0.67,0.9,0.45,0.6,2.2,0.95,1.2,0.7,0.8,18,8,8,
 25,6,21,11.5,11.5,32.5,9,24,15,15,40,12,9,0.66,0.66,0,0,10,2.33,2.33,0.66,0.33,10,4.33,
 4.33,2.33,1.66,5,1.33,1.33,5.66,1,7,3,3,7.66,2,8.66,5,5,9.33,3.66,2.33,0.66,0.33,1.33,
 1.66,4.33,2,1.33,3,2.66,6.33,3.66,3,5,4.33),nrow=5,ncol=15)
 w <- c(0.189,0.214,0.243,0.397,0.432,0.462,0.065,0.078,0.096,0.068,0.084,0.106,0.174,
 0.190,0.207)
 cb <- c('min','max','max','min','min')
 FuzzyTOPSISVector(d,w,cb)

FuzzyMCDM documentation built on May 1, 2019, 7:20 p.m.