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The entropy formula is $e_j=-h\sum_{i=1}^{m}r_{ij}\ln r_{ij}$. The constant $h$ is a scaling factor defined as $1/\ln(m)$, where $m$ is the number of alternatives, ensuring the entropy values are properly scaled.

Conditions: The decision matrix has already been normalized to produce $r_{ij}$.; The natural logarithm $\ln$ is used.; $m$ represents the total number of alternatives (rows).

The entropy formula is $e_j=-h\sum_{i=1}^{m}r_{ij}\ln r_{ij}$. The constant $h$ is a scaling factor defined as $1/\ln(m)$, where $m$ is the number of alternatives, ensuring the entropy values are properly scaled.

Conditions: The decision matrix has already been normalized to produce $r_{ij}$.; The natural logarithm $\ln$ is used.; $m$ represents the total number of alternatives (rows).

You normalize the decision matrix by dividing each entry by the sum of its own column. This column-wise operation ensures that the sum of the normalized values in each criterion column equals 1.

Conditions: The matrix contains nonnegative numerical attribute values.; The sum of each column must be nonzero.; Normalization is applied independently to each criterion column.

You normalize the decision matrix by dividing each entry by the sum of its own column. This column-wise operation ensures that the sum of the normalized values in each criterion column equals 1.

Conditions: The matrix contains nonnegative numerical attribute values.; The sum of each column must be nonzero.; Normalization is applied independently to each criterion column.