PROMETHEEOutrankingFlows: Outranking flows for the PROMETHEE methods

In MCDA: Support for the Multicriteria Decision Aiding Process

Outranking flows for the PROMETHEE methods

Description

This function computes the positive and negative outranking flows for the PROMETHEE methods. It takes as input a performance table and converts the evaluations to preference indices based on the given function types and parameters for each criterion.

Usage

PROMETHEEOutrankingFlows(
  performanceTable,
  preferenceFunction,
  preferenceThreshold,
  indifferenceThreshold,
  gaussParameter,
  criteriaWeights,
  criteriaMinMax
)

Arguments

performanceTable	Matrix containing the evaluation 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).
preferenceFunction	A vector with preference functions.preferenceFunction should be equal to Usual,U-shape,V-shape, Level,V-shape-Indiff or Gaussian. The elements are named according to the IDs of the criteria.
preferenceThreshold	A vector containing threshold of strict preference. The elements are named according to the IDs of the criteria.
indifferenceThreshold	A vector containing threshold of indifference. The elements are named according to the IDs of the criteria.
gaussParameter	A vector containing parameter of the Gaussian preference function. The elements are named according to the IDs of the 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.

Value

The function returns two vectors: The first one contains the positive outranking flows and the second one contains the negative outranking flows.

Examples

# The evaluation table

performanceTable <- rbind(
  c(1,10,1),
  c(4,20,2),
  c(2,20,0),
  c(6,40,0),
  c(30,30,3))
rownames(performanceTable) <- c("RER","METRO1","METRO2","BUS","TAXI")
colnames(performanceTable) <- c("Price","Time","Comfort")

# The preference functions 
preferenceFunction<-c("Gaussian","Level","V-shape-Indiff")

#Preference threshold
preferenceThreshold<-c(5,15,3)
names(preferenceThreshold)<-colnames(performanceTable)

#Indifference threshold
indifferenceThreshold<-c(3,11,1)
names(indifferenceThreshold)<-colnames(performanceTable)

#Parameter of the Gaussian preference function
gaussParameter<-c(4,0,0)
names(gaussParameter)<-colnames(performanceTable)

#weights

criteriaWeights<-c(0.2,0.3,0.5)
names(criteriaWeights)<-colnames(performanceTable)

# criteria to minimize or maximize

criteriaMinMax<-c("min","min","max")
names(criteriaMinMax)<-colnames(performanceTable)


# Outranking flows

outrankingFlows<-PROMETHEEOutrankingFlows(performanceTable, preferenceFunction,
                                          preferenceThreshold,indifferenceThreshold,
                                          gaussParameter,criteriaWeights,
                                          criteriaMinMax)

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