Benchmarking for Multi-Criteria Decision Analysis.

Introduction

Implements various Multi-Criteria Decision Analysis (MCDA) methods for benchmarking studies. These methods are designed to evaluate and rank alternatives based on multiple criteria, applying normalization, weighting, and aggregation techniques. The package includes a new proposed algorithm MEGAN in addition to the popular decision-making methods such as ARAS, AROMAN, COCOSO, CODAS, COPRAS, EDAS, ELECTRE family (I-IV), FUCA, GRA, MABAC, MAIRCA, MARCOS, MAUT, MAVT, MEGAN, MOORA, OCRA, ORETES, PROMETHEE family (I - VI), RAM, ROV, SMART, TOPSIS, VIKOR, WASPAS, WPM, WSM and many more, facilitating flexible and efficient analyses for multi-criteria problems.

Install mcdabench

You can easily install the mcdabench package from CRAN:

install.packages("mcdabench", dep=TRUE)

After installing the package, load it into your R session by running: library(mcdabench)

library(mcdabench)

Quickly mcdabench

Load data

data(egrids)
dmat < egrids$dmat # Decision matrix
bc <- egrids$bcvec # Benefit-cost vector
uw <- egrids$weights # Criteria weights 

## Normalization

nmatrix1 <- normalize(dmatrix, bcvec=bc, type="maxmin") nmatrix1

nmatrix2 <- normalize(dmatrix, bcvec=bc, type="sum") nmatrix2

## Calculate weights

equwei <- calcweights(nmatrix1, bcvec=bc, type="equal") equwei

entwei <- calcweights(nmatrix1, bcvec=bc, type="entropy") entwei

## Apply MCDA Methods
### Run MEGAN

resmegan <- megan(dmatrix, bcvec=bc, weights=uw, normethod="maxmin") print(resmegan)$rank

### Run TOPSIS

restopsis <- topsis(dmatrix, bcvec=bc, weights=uw, normethod="maxmin") print(restopsis)

### Run VIKOR

resvikor <- vikor(dmatrix, bcvec=bc, weights=uw, v=0.8) print(resvikor$rank)

## Weight Sensitivity Analsyis
### Gradual weight modification for Testing VIKOR

mp <- list(v=0.5) wp <- list(rp = seq(0.01, 0.5, 0.05))

vikorgrawei <- weisana(dmatrix = dmat, bcvec = bc, weimethod = "gradual", weipars = wp, mcdamethod = vikor, methodpars = mp, sensplot=FALSE) print(vikorgrawei) sensplot(vikorgrawei$sensitivity_table, mtitle="Weight Sensivity Analysis for VIKOR", colpal=terrain.colors(10))

### Test WASPAS with gradual and random weighting

mp <- list(v=0.5, normethod="linear", tiesmethod="average") wp <- list(rp = seq(0.01, 0.6, 0.01))

waspasgrawei <- weisana(dmatrix = dmat, bcvec = bc, weimethod = "gradual", weipars = wp, mcdamethod = waspas, methodpars = mp) print(waspasgrawei) sensplot(waspasgrawei$sensitivity_table, mtitle="Weight Sensivity Analysis for WASPAS", colpal=terrain.colors(10))

waspasrandwei <- weisana(dmatrix = dmat, bcvec = bc, weimethod = "random", weipars = list(ss=0.05, niters=50), mcdamethod = waspas, methodpars = mp) print(waspasrandwei) sensplot(waspasrandwei$sensitivity_table, mtitle="Weight Sensivity Analysis (random) for WASPAS", colpal=terrain.colors(10))

# Bechmarking the Methods

Sample decision matrix

dm <- matrix(c( 10, 20, 30, 1.5, 102, 55, 15, 25, 35, 1.6, 90, 60, 12, 22, 32, 1.7, 100, 58, 13, 24, 33, 1.8, 95, 57, 14, 26, 37, 1.9, 98, 59, 11, 23, 31, 1.65, 101, 56, 16, 27, 36, 1.55, 97, 61, 17, 28, 38, 1.7, 99, 63, 18, 29, 39, 1.8, 94, 62, 19, 30, 40, 1.75, 96, 64 ), nrow = 10, byrow = TRUE) colnames(dm) <- paste0("C", 1:ncol(dm)) rownames(dm) <- paste0("ALT", 1:nrow(dm))

Benefit-Cost vector

bc <- c(1, -1, 1, -1, 1, 1)

User-defined weights

userwei <- c(0.3, 0.1, 0.2, 0.1, 0.2, 0.1)

prmlist <- list( aras = list(), aroman = list(lambda = 0.5, beta = 0.5), cocoso = list(lambda = 0.5), codas = list(thr = 0.1),
smart = list(), topsis = list(normethod = "maxmin"), vikor = list(normethod = "maxmin", v = 0.8), waspas = list(normethod = "linear", v = 0.5), wpm = list(normethod = "vector"), wsm = list() )

Compare selected methods with 'gini' weights

giniwei <- calcweights(dm, bcvec=bc, type="gini") rescomp_3 <- methodbench(dmatrix = dm, bcvec = bc, weights = giniwei, mcdm = methodlist, params=prmlist) print(rescomp_3$rankmat) rankheatmap(rescomp_3$rankmat, colpal=1, cellnotes=TRUE, tcol="white")

Overall ranks and outranking

resoverall <- rankaggregate(rescomp_3$rankmat, tiesmethod="average", topk = 3, damp = 0.5, niters = 200, tol = 1e-4) print(resoverall)

# Citation

To cite the mcdabench package in publications, please run the following code in R.

citation("mcdabench")