# 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

 `1` ```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

 ```1 2 3 4 5 6 7 8``` ``` 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 29, 2017, 5:41 p.m.