Francisco Jesús Martínez Mimbrera
39 lines
1.7 KiB
R
39 lines
1.7 KiB
R
#' calculateWeights
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#'
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#' Implementation based on https://doi.org/10.1016/j.omega.2015.12.001.
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#' Calculates weights of the criteria using a linear model.
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#' Steps:
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#' 1. Build model (consists of validating model and constructing necessary constraints for LP problem).
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#' 2. Solve LP problem.
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#' 3. Calculate consistency ratio.
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#'
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#' @name calculateWeights
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#' @param criteriaNames Names of the criteria
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#' @param bestToOthers Vector of pairwise comparisons. Best criterion should be 1, others <2, 9>.
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#' @param othersToWorst Vector of pairwise comparisons. Worst criterion should be 1, others <2, 9>.
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#' @return Result that consist of \code{criteriaNames}, \code{criteriaWeights}, \code{consistencyRatio} and a model that was used to calculate weights.
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#' @examples
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#' criteriaNames <- c("quality", "price", "comfort", "safety", "style")
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#' bestToOthers <- c(2, 1, 4, 2, 8)
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#' othersToWorst <- c(4, 8, 2, 4, 1)
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#' calculateWeights(criteriaNames, bestToOthers, othersToWorst)
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#' @import Rglpk
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#' @export
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calculateWeights <- function(criteriaNames, bestToOthers, othersToWorst){
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model <- buildModel(bestToOthers, othersToWorst, criteriaNames)
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#const values that are listed in the article
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consistencyIndex <- c(0, .44, 1.0, 1.63, 2.3, 3., 3.73, 4.47, 5.23)
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#unique optimal solution
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result <- solveLP(model)
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weights <- result$solution[1:model$ksiIndex-1]
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consistencyRatio <- result$solution[model$ksiIndex] / consistencyIndex[as.integer(model$a_bw)]
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result <- list(criteriaNames = criteriaNames, criteriaWeights = weights, consistencyRatio = consistencyRatio)
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list(result = result, model = model)
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}
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solveLP <- function(model){
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Rglpk_solve_LP(model$objective, model$constraints$lhs, model$constraints$dir, model$constraints$rhs, max = model$maximize)
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}
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