AHP.Unif: Probabilistic AHP using Uniform distributions

In CPP: Composition of Probabilistic Preferences (CPP)

Description

This function computes criteria weights, using AHP and randomic pair-wise evaluations by Uniform distributions.

Usage

AHP.Unif(n, list)

Arguments

n	Random numbers created from Uniform distributions, using the parameters 'min' and 'max' of each pair-wise criteria comparison elicited from the experts.
list	Pair-wise comparison matrices of expert opinions. The function 'list' is embedded in R.

Value

Weights returned from a simulation of AHP with Uniform distributions. The weights are driven from the simulated matrix that gives the minimum AHP Consistent Index.

References

Saaty, Thomas L. (1980). The analytic hierarchy process: planning, priority setting, resource allocation, McGraw-Hill.

Examples

n=5000 # Simulation
# Expert pair-wise evaluations
Exp.1 = matrix(c(1,0.2,0.3,5,1,0.2,3,5,1),3,3)
Exp.2 = matrix(c(1,2,8,0.5,1,6,0.12,0.16,1),3,3)
Exp.3 = matrix(c(1,0.5,0.5,2,1,6,2,0.16,1),3,3)
Exp.4 = matrix(c(1,3,4,0.3,1,0.5,0.25,0.3,1),3,3)
Exp.5 = matrix(c(1,4,5,0.25,1,1,0.2,1,1),3,3)
list = list(Exp.1,Exp.2,Exp.3,Exp.4,Exp.5)
AHP.Unif(n,list)

Example output

Loading required package: ineq
Loading required package: kappalab
Loading required package: lpSolve
Loading required package: quadprog
Loading required package: kernlab

Attaching package: ‘kappalab’

The following object is masked from ‘package:ineq’:

    entropy

Loading required package: mc2d
Loading required package: mvtnorm

Attaching package: ‘mc2d’

The following objects are masked from ‘package:base’:

    pmax, pmin

  Weight1   Weight2   Weight3 
0.2731867 0.2349819 0.4918314 

CPP documentation built on May 2, 2019, 1:34 p.m.