Strategy Concepts

Increasing

The increasing strategy rescales the criteria values using min-max normalization to determine its score, then multiplies it by it's weight. This typically returns a value between 0 and 1.

As the input increases, the score increases.

$$score = weight(\frac{x_i-min(x)}{max(x)-min(x)})$$

$max(x)$ - represents the maximum value found for the specified critera in your data set.

$min(x)$ - represents the maximum value found for the specified critera in your data set.

Example:

const min = 50const max = 60const xi = 53const weight = 0.5const score = weight * (xi - min) / (max - min)
console.log(score) // 0.15

Decreasing

The decreasing strategy rescales the criteria values using reversed min-max normalization to determine its score, then multiplies it by it's weight. This typically returns a value between 0 and 1.

As the input decreases, the score increases.

$$score = weight(\frac{max(x)-x_i}{max(x)-min(x)})$$

$max(x)$ - represents the maximum value found for the specified critera in your data set.

$min(x)$ - represents the maximum value found for the specified critera in your data set.

Example:

const min = 50const max = 60const xi = 53const weight = 0.5const score = weight * (max - xi) / (max - min)
console.log(score) // 0.35