MCDA (version 0.1.0)

SRMP: SRMP: a simple ranking method using reference profiles

Description

SRMP is a ranking method that uses dominating reference profiles, in a given lexicographic ordering, in order to output a total preorder of a set of alternatives.

Usage

SRMP(
  performanceTable,
  referenceProfiles,
  lexicographicOrder,
  criteriaWeights,
  criteriaMinMax,
  alternativesIDs = NULL,
  criteriaIDs = NULL
)

Value

The function returns a vector containing the ranks of the alternatives (the higher the better).

Arguments

performanceTable

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

referenceProfiles

Matrix containing, in each row, the reference profiles. The columns are named according to the criteria.

lexicographicOrder

A vector containing the indexes of the reference profiles in a given order. This vetor needs to be of the same length as the number of rows in referenceProfiles and it has to contain a permutation of the indices of these rows.

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 datashould be filtered.

criteriaIDs

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

References

A. Rolland. Procédures d’agrégation ordinale de préférences avec points de référence pour l’aide a la décision. PhD thesis, Université Paris VI, 2008.

Examples

# the performance table

performanceTable <- rbind(c(10,10,9),c(10,9,10),c(9,10,10),c(9,9,10),c(9,10,9),c(10,9,9),
                          c(10,10,7),c(10,7,10),c(7,10,10),c(9,9,17),c(9,17,9),c(17,9,9),
                          c(7,10,17),c(10,17,7),c(17,7,10),c(7,17,10),c(17,10,7),c(10,7,17),
                          c(7,9,17),c(9,17,7),c(17,7,9),c(7,17,9),c(17,9,7),c(9,7,17))

referenceProfiles <- rbind(c(5,5,5),c(10,10,10),c(15,15,15))

lexicographicOrder <- c(2,1,3)

weights <- c(0.2,0.44,0.36)

criteriaMinMax <- c("max","max","max")

rownames(performanceTable) <- c("a1","a2","a3","a4","a5","a6","a7","a8","a9","a10","a11","a12",
                                "a13","a14","a15","a16","a17","a18","a19","a20","a21","a22",
                                "a23","a24")

colnames(performanceTable) <- c("c1","c2","c3")

colnames(referenceProfiles) <- c("c1","c2","c3")

names(weights) <- c("c1","c2","c3")

names(criteriaMinMax) <- colnames(performanceTable)

expectedpreorder <- list('a16','a13',c('a3','a9'),'a14','a17',c('a1','a7'),'a18','a15',
                         c('a2','a8'),c('a11','a20','a22'),'a5',c('a10','a19','a24'),
                         'a4',c('a12','a21','a23'),'a6')

preorder<-SRMP(performanceTable, referenceProfiles, lexicographicOrder, weights, criteriaMinMax)