FuzzyWASPAS: Implementation of Fuzzy WASPAS Method for Multi-Criteria...

In FuzzyMCDM: Multi-Criteria Decision Making Methods for Fuzzy Data

Description

The FuzzyWASPAS function implements the Fuzzy Weighted Aggregated Sum Product ASsessment (Fuzzy WASPAS) Method.

Usage

FuzzyWASPAS(decision, weights, cb, lambda)

Arguments

decision	The decision matrix (m x (n*3)) with the values of the m alternatives, for the n criteria, and multiplied by 3 since they are triangular fuzzy numbers.
weights	A vector of length n*3, containing the fuzzy weights for the criteria.
cb	A vector of length n. Each component is either cb(i)='max' if the i-th criterion is benefit or cb(i)='min' if the i-th criterion is a cost.
lambda	A value in [0,1]. It is used in the calculation of the W index.

Value

FuzzyWASPAS returns a data frame which contains the score of the W index and the ranking of the alternatives.

References

Turskis, Z. and Zavadskas, E. K. and Antucheviciene, J. and Kosareva, N. A Hybrid Model Based on Fuzzy AHP and Fuzzy WASPAS for Construction Site Selection. International Journal of Computers Communications & Control, 10(6), 873-888, 2015.

Examples

 d <- matrix(c(0.5,0.6,0.6,0.6,0.6,0.7,0.7,0.7,0.7,0.8,0.8,0.8,0.6,0.6,0.8,0.5,0.7,0.7,
 0.9,0.6,0.8,0.8,1,0.7,0.8,0.5,0.6,0.6,0.9,0.6,0.7,0.7,1,0.7,0.8,0.8,0.5,0.6,0.5,0.4,0.6,
 0.7,0.6,0.5,0.7,0.8,0.7,0.6,0.8,0.7,0.6,0.5,0.9,0.8,0.7,0.6,1,0.9,0.8,0.7,0.5,0.8,0.6,
 0.8,0.6,0.9,0.7,0.9,0.7,1,0.8,1,0.4,0.5,0.8,0.7,0.5,0.6,0.9,0.8,0.6,0.7,1,0.9,0.5,0.4,
 0.4,0.5,0.6,0.5,0.5,0.6,0.7,0.6,0.6,0.7),nrow=4,ncol=24)
 w <- c(0.21,0.28,0.35,0.16,0.20,0.23,0.14,0.16,0.17,0.09,0.12,0.17,0.07,0.08,0.12,0.05,
 0.06,0.09,0.03,0.05,0.07,0.01,0.03,0.06)
 cb <- c('max','max','max','max','max','max','max','max')
 lambda <- 0.49
 FuzzyWASPAS(d,w,cb,lambda)

FuzzyMCDM documentation built on May 1, 2019, 7:20 p.m.