### 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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