# MetaRanking: Implementation of MetaRanking function for Multi-Criteria... In FuzzyMCDM: Multi-Criteria Decision Making Methods for Fuzzy Data

## Description

The `MetaRanking` function internally calls functions `FuzzyMMOORA`, `FuzzyTOPSISLinear`, `FuzzyTOPSISVector`, `FuzzyVIKOR` and `FuzzyWASPAS` and then calculates a sum of the their rankings and an aggregated ranking by applying the `RankAggreg` package.

## Usage

 `1` ```MetaRanking(decision, weights, cb, lambda, v) ```

## Arguments

 `decision` The decision matrix (m x n) with the values of the m alternatives, for the n criteria. `weights` A vector of length n, containing the weights for the criteria. The sum of the weights has to be 1. `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. `lambda` A value in [0,1]. It is used in the calculation of the W index for WASPAS method. `v` A value in [0,1]. It is used in the calculation of the Q index for VIKOR method.

## Value

`MetaRanking` returns a data frame which contains the rankings of the Fuzzy Multi-MOORA, Fuzzy TOPSIS (linear transformation and vectorial normalization), Fuzzy VIKOR, Fuzzy WASPAS Methods and the MetaRankings of the alternatives.

## Examples

 ``` 1 2 3 4 5 6 7 8 9 10 11 12``` ``` 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') lambda <- 0.5 v <- 0.5 MetaRanking(d,w,cb,lambda,v) ```

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