@@ -133,7 +133,7 @@ The weighted-product method is easier to use and can capture user preferences mo

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 score $s$ of this objective vector is calculated using the weighted product according to the following formula.

The score $s$ of an objective vector is calculated using the weighted product according to the following formula.

Some of the properties of the weighted-product method relevant for c2 scheduling are as follows.

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@@ -157,7 +157,7 @@ For a derivation of this estimation, see \citet{Tofallis2014}.

\Cref{sec:examples} has an example that demonstrates how different weights can guide our algorithm towards different solutions.

\paragraph{Measurement theory}

All objectives must be units of ratio scale.

In order to utilize the weighted-product method, all objectives must be units of ratio scale.

For a unit of measurement to be of ratio scale, it must have a meaningful zero value that represents absence of the measured quantity, and must allow multiplication by positive constants.

This allows comparisons based on ratios of measurements, like "this is twice as long as that".

Examples are mass, length, time, angle, money, percentage, and temperature in Kelvin.