MCDM (version 1.0)

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

Description

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

Usage

WASPAS(decision, weights, cb, lambda)

Arguments

decision

The decision matrix (m x n) with the values of the m alternatives, for the n criteria.

weights

A vector of length n, containing the weights for the criteria. The sum of the weights has to be 1.

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

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

References

Zavadskas, E. K.; Turskis, Z.; Antucheviciene, J.; Zakarevicius, A. Optimization of Weighted Aggregated Sum Product Assessment. Electronics and Electrical Engineering, 122(6), 3-6, 2012.

Examples

d <- matrix(rpois(12, 5), nrow = 4)
 w <- c(0.2, 0.2, 0.6)
 cb <- c('max','min','max')
 lambda <- 0.5
 WASPAS(d,w,cb,lambda)