*3.2. Entropy Weighting Method*

This method measures the uncertainty in the information, and the criteria weights are given by Equation (3).

$$w\_j = \frac{1 - E\_j}{\sum\_{j=1}^{n} (1 - E\_j)}\tag{3}$$

In the equation above, 1—*Ej* represents the degree of diversity of the information related to the *j*-th criterion and *Ej* is the entropy value of the *j*-th criterion, given by Equation (4). In this equation, *pij* are the normalized data, Equation (5).

$$E\_j = -\frac{\sum\_{i=1}^{m} p\_{ij} \ln(p\_{ij})}{\ln(m)}\tag{4}$$

$$p\_{ij} = \frac{\mathcal{V}\_{ij}}{\sum\_{i=1}^{m} \mathcal{V}\_{ij}} \tag{5}$$

According to the equations above, the range of the entropy value is 0–1. A low entropy value indicates that the degree of disorder corresponding to criterion *j* is low and thus leads to a high weight.
