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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -1,8 +1,8 @@ | ||
| Package: MCDM | ||
| Type: Package | ||
| Title: Multi-Criteria Decision Making Methods for Crisp Data | ||
| Version: 1.1 | ||
| Date: 2016-09-05 | ||
| Version: 1.2 | ||
| Date: 2016-09-21 | ||
| Author: Blanca A. Ceballos Martin <[email protected]> | ||
| Maintainer: Blanca A. Ceballos Martin <[email protected]> | ||
| Description: Implementation of several MCDM methos for crisp data for decision | ||
|
|
@@ -16,6 +16,6 @@ URL: http://decsai.ugr.es/index.php?p=miembros&id=19909 | |
| LazyData: true | ||
| RoxygenNote: 5.0.1 | ||
| NeedsCompilation: no | ||
| Packaged: 2016-09-05 11:31:04 UTC; Modo | ||
| Packaged: 2016-09-22 08:31:35 UTC; Modo | ||
| Repository: CRAN | ||
| Date/Publication: 2016-09-05 13:53:38 | ||
| Date/Publication: 2016-09-22 16:50:45 | ||
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -1,74 +1,72 @@ | ||
| #' Implementation of TOPSIS Method for Multi-Criteria Decision Making Problems. | ||
| #' | ||
| #' @description The \code{TOPSISLinear} function implements the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) Method with the linear transformation of maximum as normalization prodecure. | ||
| #' @param decision The decision matrix (\emph{m} x \emph{n}) with the values of the \emph{m} alternatives, for the \emph{n} criteria. | ||
| #' @param weights A vector of length \emph{n}, containing the weights for the criteria. The sum of the weights has to be 1. | ||
| #' @param cb A vector of length \emph{n}. Each component is either \code{cb(i)='max'} if the \emph{i-th} criterion is benefit or \code{cb(i)='min'} if the \emph{i-th} criterion is a cost. | ||
| #' @return \code{TOPSISLinear} 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. On rank reversal and TOPSIS method. Mathematical and Computer Modelling, 56(5-6), 123-132, 2012. | ||
| #' @examples | ||
| #' | ||
| #' d <- matrix(c(1,4,3,5,2,3),nrow = 3,ncol = 2) | ||
| #' w <- c(0.5,0.5) | ||
| #' cb <- c('max','max') | ||
| #' TOPSISLinear(d,w,cb) | ||
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| TOPSISLinear <- function(decision, #matrix with all the alternatives | ||
| weights, #vector with the numeric values of the weights | ||
| cb #vector with the "type" of the criteria (benefit = "max", cost = "min") | ||
| ) | ||
| { | ||
| #Checking the arguments | ||
| if(! is.matrix(decision)) | ||
| stop("'decision' must be a matrix with the values of the alternatives") | ||
| if(missing(weights)) | ||
| stop("a vector containing n weigths, adding up to 1, should be provided") | ||
| if(sum(weights) != 1) | ||
| stop("The sum of 'weights' is not equal to 1") | ||
| if(! is.character(cb)) | ||
| stop("'cb' must be a character vector with the type of the criteria") | ||
| if(! all(cb == "max" | cb == "min")) | ||
| stop("'cb' should contain only 'max' or 'min'") | ||
| if(length(weights) != ncol(decision)) | ||
| stop("length of 'weights' does not match the number of the criteria") | ||
| if(length(cb) != ncol(decision)) | ||
| stop("length of 'cb' does not match the number of the criteria") | ||
|
|
||
| #1. Normalization and weighting | ||
| N <- matrix(nrow = nrow(decision), ncol = ncol(decision)) | ||
|
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| Norm <- as.integer(cb == "max") * apply(decision, 2, max) + | ||
| as.integer(cb == "min") * apply(decision, 2, min) | ||
|
|
||
| N <- matrix(nrow = nrow(decision), ncol = ncol(decision)) | ||
| for(j in 1:ncol(decision)){ | ||
| if (cb[j] == 'max'){ | ||
| N[,j] <- decision[,j] / Norm[j] | ||
| } | ||
| else{ | ||
| N[,j] <- Norm[j] / decision[,j] | ||
| } | ||
| } | ||
| W <- diag(weights) | ||
| NW <- N%*%W | ||
|
|
||
| #2. Ideal solutions | ||
| posI <- as.integer(cb == "max") * apply(NW, 2, max) + | ||
| as.integer(cb == "min") * apply(NW, 2, min) | ||
| negI <- as.integer(cb == "min") * apply(NW, 2, max) + | ||
| as.integer(cb == "max") * apply(NW, 2, min) | ||
|
|
||
| #3. Distances to the ideal solutions | ||
| distance =function(x,y){ | ||
| sqrt(sum((x - y) ^ 2)) | ||
| } | ||
| posDis <- apply(NW, 1, distance, posI) | ||
| negDis <- apply(NW, 1, distance, negI) | ||
|
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| #4. R index | ||
| R <- negDis/(negDis+posDis) | ||
|
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| #5. Rank the alternatives | ||
| return(data.frame(Alternatives = 1:nrow(decision), R = R, Ranking = rank(-R, ties.method= "first"))) | ||
|
|
||
| } | ||
| #' Implementation of TOPSIS Method for Multi-Criteria Decision Making Problems. | ||
| #' | ||
| #' @description The \code{TOPSISLinear} function implements the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) Method with the linear transformation of maximum as normalization prodecure. | ||
| #' @param decision The decision matrix (\emph{m} x \emph{n}) with the values of the \emph{m} alternatives, for the \emph{n} criteria. | ||
| #' @param weights A vector of length \emph{n}, containing the weights for the criteria. The sum of the weights has to be 1. | ||
| #' @param cb A vector of length \emph{n}. Each component is either \code{cb(i)='max'} if the \emph{i-th} criterion is benefit or \code{cb(i)='min'} if the \emph{i-th} criterion is a cost. | ||
| #' @return \code{TOPSISLinear} 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. On rank reversal and TOPSIS method. Mathematical and Computer Modelling, 56(5-6), 123-132, 2012. | ||
| #' @examples | ||
| #' | ||
| #' d <- matrix(c(1,4,3,5,2,3),nrow = 3,ncol = 2) | ||
| #' w <- c(0.5,0.5) | ||
| #' cb <- c('max','max') | ||
| #' TOPSISLinear(d,w,cb) | ||
|
|
||
| TOPSISLinear <- function(decision, #matrix with all the alternatives | ||
| weights, #vector with the numeric values of the weights | ||
| cb #vector with the "type" of the criteria (benefit = "max", cost = "min") | ||
| ) | ||
| { | ||
| #Checking the arguments | ||
| if(! is.matrix(decision)) | ||
| stop("'decision' must be a matrix with the values of the alternatives") | ||
| if(missing(weights)) | ||
| stop("a vector containing n weigths, adding up to 1, should be provided") | ||
| if(sum(weights) != 1) | ||
| stop("The sum of 'weights' is not equal to 1") | ||
| if(! is.character(cb)) | ||
| stop("'cb' must be a character vector with the type of the criteria") | ||
| if(! all(cb == "max" | cb == "min")) | ||
| stop("'cb' should contain only 'max' or 'min'") | ||
| if(length(weights) != ncol(decision)) | ||
| stop("length of 'weights' does not match the number of the criteria") | ||
| if(length(cb) != ncol(decision)) | ||
| stop("length of 'cb' does not match the number of the criteria") | ||
|
|
||
| #1. Normalization and weighting | ||
| N <- matrix(nrow = nrow(decision), ncol = ncol(decision)) | ||
|
|
||
| Norm <- as.integer(cb == "max") * apply(decision, 2, max) + | ||
| as.integer(cb == "min") * apply(decision, 2, min) | ||
|
|
||
| N <- matrix(nrow = nrow(decision), ncol = ncol(decision)) | ||
| for(j in 1:ncol(decision)){ | ||
| if (cb[j] == 'max'){ | ||
| N[,j] <- decision[,j] / Norm[j] | ||
| } | ||
| else{ | ||
| N[,j] <- Norm[j] / decision[,j] | ||
| } | ||
| } | ||
| W <- diag(weights) | ||
| NW <- N%*%W | ||
|
|
||
| #2. Ideal solutions | ||
| posI <- apply(NW, 2, max) | ||
| negI <- apply(NW, 2, min) | ||
|
|
||
| #3. Distances to the ideal solutions | ||
| distance =function(x,y){ | ||
| sqrt(sum((x - y) ^ 2)) | ||
| } | ||
| posDis <- apply(NW, 1, distance, posI) | ||
| negDis <- apply(NW, 1, distance, negI) | ||
|
|
||
| #4. R index | ||
| R <- negDis/(negDis+posDis) | ||
|
|
||
| #5. Rank the alternatives | ||
| return(data.frame(Alternatives = 1:nrow(decision), R = R, Ranking = rank(-R, ties.method= "first"))) | ||
|
|
||
| } |
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