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

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

 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 2, 2019, 4:19 a.m.