Data preparation

Let’s start by preparing the data — this is the essential first step in any multi-criteria decision analysis. Your data needs to be well-organized, consistent, and complete to ensure that the results are meaningful and useful for decision-making.

note

More about input data can be read on theory docs

Decision Matrix ($M$)

The matrix is built as follows:

• rows : alternatives
• columns : decision criterion

double[,] matrix = 
{
    { 120, 1500 }, 
    { 2000, 1800 }
}

info

The data in the matrix may not be normalized, in further steps when the MCDA method is created you will be able to choose how the matrix will be normalized.

Weights Vector ($W$)

The weights have to meet the assumption $\sum_{i=1}^{n} w_i = 1$ where $n$ is the number of criteria (i.e. the number of columns in the decision matrix).

double[] weights = [0.5,0.5]

Criteria Decision Types Vector ($C$)

They will represent as vector of $\{ w_1, w_2, \dots, w_n \} \in [-1, 1]$, where $n$ is the number of criteria.

• 1 for benefit criteria (the higher, the better)
• -1 for cost criteria (the lower, the better)

int[] criteriaTypes = [-1,-1]