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

### Description

The `WASPAS`

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

### Usage

1 |

### Arguments

`decision` |
The decision matrix ( |

`weights` |
A vector of length |

`cb` |
A vector of length |

`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)
``` |