R/COPRAS.R

In RMCDA: Multi-Criteria Decision Analysis

#' Apply COmplex PRoportional ASsessment (COPRAS) method
#'
#' @param mat is a matrix and contains the values for different properties
#' of different alternatives
#' @param weights are the weights of each property in the decision making process
#' @param beneficial.vector is a vector that contains the column number of beneficial
#' properties.
#'
#' @return a vector containing the calculated quantitative utility
#'
#'
#' @examples
#' mat <- matrix(c(75.5, 95, 770, 187, 179, 239, 237,
#' 420, 91, 1365, 1120, 875, 1190, 200,
#' 74.2, 70, 189, 210, 112, 217, 112,
#' 2.8, 2.68, 7.9, 7.9, 4.43, 8.51, 8.53,
#' 21.4, 22.1, 16.9, 14.4, 9.4, 11.5, 19.9,
#' 0.37, 0.33, 0.04, 0.03, 0.016, 0.31, 0.29,
#' 0.16, 0.16, 0.08, 0.08, 0.09, 0.07, 0.06), nrow=7)
#' colnames(mat)<-c("Toughness Index",	"Yield Strength",	"Young's Modulus",
#' "Density",	"Thermal Expansion",	"Thermal Conductivity",	"Specific Heat")
#' rownames(mat)<-c("AI 2024-T6",
#' "AI 5052-O",
#' "SS 301 FH",
#' "SS 310-3AH",
#' "Ti-6AI-4V",
#' "Inconel 718",
#' "70Cu-30Zn")
#' weights <- c(0.28, 0.14, 0.05, 0.24, 0.19, 0.05, 0.05)
#' beneficial.vector<-c(1,2,3)
#' apply.COPRAS(mat, weights, beneficial.vector)
#' @export apply.COPRAS
apply.COPRAS <- function(mat, weights, beneficial.vector){

  normalized.mat <- t(t(mat)/colSums(mat))

  normalized.mat <- t(weights*t(normalized.mat))

  S_pos <- rowSums(normalized.mat[,beneficial.vector])

  S_neg <- rowSums(normalized.mat[,-beneficial.vector])

  Q_i <- S_pos + (min(S_neg)*sum(S_neg))/(S_neg*sum(min(S_neg)/S_neg))

  U_i <- Q_i/max(Q_i)

  return(U_i)

}