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

In MCDM: Multi-Criteria Decision Making Methods for Crisp Data

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 WSM, WPM and the Q 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(c(370,314,480,850,11,7,10,16,2.69,2.37,3.09,3.17,2.75,3.27,3.67,4.10,
 5,35,30,50,1.63,1.72,1.87,1.91,1.47,2.07,1.38,2.22,7.11,5.60,7.82,8.25,88,12.60,94,
 23,410,100,410,65,2.93,2.13,2.87,1.10,1.98,3.21,2.94,4.37),nrow = 4,ncol = 12)
 w <- c(0.0626,0.0508,0.1114,0.0874,0.0625,0.1183,0.0784,0.0984,0.053,0.1417,
 0.0798,0.0557)
 cb <- c('min','min','max','max','max','max','max','max','min','min','max','max')
 lambda <- 0.5
 WASPAS(d,w,cb,lambda)

MCDM documentation built on May 2, 2019, 4:19 a.m.