FuzzyMCDM (version 1.1)

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

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)