MARE {MCDA}

Multi-Attribute Range Evaluations (MARE)

Description

MARE is a multi-criteria decision analysis method which was originally developed by Hodgett et al. in 2014.

Usage

MARE(
  performanceTableMin,
  performanceTable,
  performanceTableMax,
  criteriaWeights,
  criteriaMinMax,
  alternativesIDs = NULL,
  criteriaIDs = NULL
)

Arguments

performanceTableMin

Matrix or data frame containing the minimum performance table. Each column corresponds to an alternative, and each row to a criterion. Columns (resp. rows) must be named according to the IDs of the alternatives (resp. criteria).

performanceTable

Matrix or data frame containing the most likely performance table. Each column corresponds to an alternative, and each row to a criterion. Columns (resp. rows) must be named according to the IDs of the alternatives (resp. criteria).

performanceTableMax

Matrix or data frame containing the maximum performance table. Each column corresponds to an alternative, and each row to a criterion. Columns (resp. rows) must be named according to the IDs of the alternatives (resp. criteria).

criteriaWeights

Vector containing the weights of the criteria. The elements are named according to the IDs of the criteria.

criteriaMinMax

Vector containing the preference direction on each of the criteria. "min" (resp. "max") indicates that the criterion has to be minimized (maximized). The elements are named according to the IDs of the criteria.

alternativesIDs

Vector containing IDs of alternatives, according to which the data should be filtered.

criteriaIDs

Vector containing IDs of criteria, according to which the data should be filtered.

Value

The function returns an element of type mare which contains the MARE scores for each alternative.

References

Richard E. Hodgett, Elaine B. Martin, Gary Montague, Mark Talford (2014). Handling uncertain decisions in whole process design. Production Planning & Control, Volume 25, Issue 12, 1028-1038.

Examples

performanceTableMin <- t(matrix(c(78,87,79,19,8,68,74,8,90,89,74.5,9,20,81,30),
                  nrow=3,ncol=5, byrow=TRUE)) 
performanceTable <- t(matrix(c(80,87,86,19,8,70,74,10,90,89,75,9,33,82,30),
                              nrow=3,ncol=5, byrow=TRUE))
performanceTableMax <- t(matrix(c(81,87,95,19,8,72,74,15,90,89,75.5,9,36,84,30),
                                 nrow=3,ncol=5, byrow=TRUE))  

row.names(performanceTable) <- c("Yield","Toxicity","Cost","Separation","Odour")
colnames(performanceTable) <- c("Route One","Route Two","Route Three")
row.names(performanceTableMin) <- row.names(performanceTable)
colnames(performanceTableMin) <- colnames(performanceTable)
row.names(performanceTableMax) <- row.names(performanceTable)
colnames(performanceTableMax) <- colnames(performanceTable)

weights <- c(0.339,0.077,0.434,0.127,0.023) 
names(weights) <- row.names(performanceTable)

criteriaMinMax <- c("max", "max", "max", "max", "max")
names(criteriaMinMax) <- row.names(performanceTable)

overall1 <- MARE(performanceTableMin, 
                   performanceTable, 
                   performanceTableMax, 
                   weights, 
                   criteriaMinMax)

overall2 <- MARE(performanceTableMin, 
                    performanceTable,
                    performanceTableMax,
                    weights,
                    criteriaMinMax,
                    alternativesIDs = c("Route Two","Route Three"),
                    criteriaIDs = c("Yield","Toxicity","Cost","Separation"))