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

## Description

The `MetaRanking` function internally calls functions `MMOORA`, `RIM`, `TOPSISLinear`, `TOPSISVector`, `VIKOR` and `WASPAS` 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, AB, CD) ```

## 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. `AB` A matrix (2 x n). AB[1,] corresponds with the A extrem, and AB[2,] represents the B extrem of the domain of each criterion. `CD` A matrix (2 x n). CD[1,] corresponds with the C extrem, and CD[2,] represents the D extrem of the ideal reference of each criterion.

## Value

`MetaRanking` returns a data frame which contains the rankings of the Multi-MOORA, RIM, TOPSISLinear, TOPSISVector, VIKOR, WASPAS Methods and the both MetaRankings of the alternatives.

## Examples

 ```1 2 3 4 5 6 7 8``` ``` d <- matrix(c(1,2,5,3000,3750,4500),nrow = 3,ncol = 2) w <- c(0.5,0.5) cb <- c('min','max') lambda <- 0.5 v <- 0.5 AB <- matrix(c(1,5,3000,4500),nrow = 2,ncol=2) CD <- matrix(c(1,1,4500,4500),nrow = 2,ncol=2) MetaRanking(d,w,cb,lambda,v,AB,CD) ```

MCDM documentation built on May 29, 2017, 5:41 p.m.