SRMP: SRMP: a simple ranking method using reference profiles

In MCDA: Support for the Multicriteria Decision Aiding Process

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
)

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.

Value

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

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)

MCDA documentation built on Nov. 24, 2023, 5:10 p.m.