Description Usage Arguments Value Author(s) References See Also Examples
Sensitivity Analysis is a method developed by Wolters & Mareschal (1995) to
evaluate how RPrometheeII
and RPrometheeIV
results are sensitive to changes in weights of criterias. That is, how the
solution to the decision problem can be affected by the distribution of
criterias weights.
1 | SensitivityAnalysis(RPrometheeArguments, method = "RPrometheeII")
|
RPrometheeArguments |
An object with all RPromethee arguments. For
PROMETHEE IV, it's important that |
method |
A character object used to choose how the SensitivityAnalysis is going to be calculated. The method can be |
Solution The solution resulting from linear programming problem.
alternatives The alternatives names.
criterias The criterias names.
datMat The data used corresponding to criterias and alternatives.
Pedro Henrique Melo Albuquerque, pedroa@unb.br
Gustavo Monteiro Pereira, monteirogustavop@gmail.com
M. Behzadian et al.
PROMETHEE: A comprehensive literature review on methodologies and
applications
European Journal of Operational Research v. 200, p.198-215, 2010.
https://www.sciencedirect.com/science/article/abs/pii/S0377221709000071
J. P. Brans, Ph. Vincke
A Preference Ranking Organisation Method: (The PROMETHEE Method
for Multiple Criteria Decision-Making)
Management science, v. 31, n. 6, p. 647-656, 1985.
https://pdfs.semanticscholar.org/edd6/f5ae9c1bfb2fdd5c9a5d66e56bdb22770460.pdf
J. P. Brans, B. Mareschal
PROMETHEE methods. In: Figueria J, Greco S, Ehrgott M (eds)
Multiple criteria decision analysis: state of the art surveys.
Springer Science, Business Media Inc., Boston pp 163???195.
http://www.springer.com/la/book/9780387230818
W.T.M. Wolters, B. Mareschal
Novel types of sensitivity analysis for additive MCDM
ethods.
European Journal of Operational Research, v. 81, p. 281-290, 1995.
https://www.sciencedirect.com/science/article/abs/pii/0377221793E0343V
Other RPromethee methods: PrometheeIIIPlot
,
PrometheeIIPlot
,
PrometheeIPlot
,
PrometheeIVPlot
,
RPrometheeConstructor
,
RPrometheeIII
, RPrometheeII
,
RPrometheeIVKernel
,
RPrometheeIV
, RPrometheeI
,
RPrometheeV
,
UpdateRPrometheeAlternatives
,
UpdateRPrometheeArguments
,
WalkingWeightsPlot
,
plot,RPrometheeI-method
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 | ## Create objects for each argument
data <- matrix(c(5.2, -3.5,
4.3, -1.2,
6.7, -2.0,
5.4, -5.0,
4.8, 0.0,
2.8, -3.4), byrow = TRUE, ncol = 2)
parms<-matrix(c(1.0, -2.3), byrow = TRUE, ncol = 1, nrow = 2)
vecWeights <- c(0.3, 0.7)
vecMaximiz <- c(FALSE, TRUE)
prefFunction <- c(0, 0)
constraintDir <- rep("<=", ncol(data))
bounds <- c(7,-1)
normalize <- FALSE
alternatives <- c("Alt 1", "Alt 2", "Alt 3")
## Create RPrometheeArguments object
PromObj <- RPrometheeConstructor(datMat = data, vecWeights = vecWeights,
vecMaximiz = vecMaximiz, prefFunction = prefFunction, parms = parms,
normalize = normalize, alternatives = alternatives, bounds = bounds,
constraintDir = constraintDir)
## Run RPrometheeV using standard method ("RPrometheeII")
(result <- SensitivityAnalysis(PromObj))
## Run RPrometheeV using RPrometheeIV
(result <- SensitivityAnalysis(PromObj, "RPrometheeIV"))
## Updating alternatives name using UpdateRPrometheeAlternatives
newAlternatives <- c("A", "B", "C", "D", "E", "F")
result <- UpdateRPrometheeAlternatives(result, newAlternatives)
## Updating any argument using UpdateRPrometheeArguments
newParms <- matrix(c(1.6, 4.2), byrow = TRUE, ncol = 1)
PromObj <- UpdateRPrometheeArguments(PromObj, "parms", newParms)
(result <- SensitivityAnalysis(PromObj))
|
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