Commit 5491836b authored by Markus Klinik's avatar Markus Klinik
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mixed units of measurements

parent 13c7238f
......@@ -133,12 +133,16 @@ The weighted-product method does not require normalization and as such is not su
Let $\vec{q} = \tuple{p_1, p_2, \ldots, q_1, q_2, \ldots}$ be an objective vector where the $p_i$ are more-is-better objectives and the $q_i$ are less-is-better objectives.
Let $\vec{w} = \tuple{w_1, w_2, \ldots, v_1, v_2, \ldots}$ be the weights, pre-determined by the decision maker.
The weighted product of the objective vector is calculated with the following formula.
$$(p_1^{w_1} p_2^{w_2} \ldots) / (q_1^{v_2} q_2^{v_2} \ldots)$$
The score $s$ of this objective vector is calculated using the weighted product according to the following formula.
$$s = (p_1^{w_1} p_2^{w_2} \ldots) / (q_1^{v_2} q_2^{v_2} \ldots)$$
Some of the properties of the weighted-product method relevant for c2 scheduling are as follows.
\paragraph{Mixed units of measurement}
This is the main reason why we chose the weighted-product method.
The units and magnitudes of the individual objectives does not matter.
One objective can be a distance in meters, another one a cost in euros, yet another one a time in seconds.
No conversion or normalization is required, the numbers can be multiplied as-is.
\paragraph{Weights allow for diminishing returns}
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