When the importance levels are equal, the satisfaction degrees of objective functions are very close to each other, in some cases.
Therefore, the core of our framework stresses the more subjective, perceived communication behaviour and people's evaluations of that behaviour (i.e. satisfaction), instead of objective, observable behaviour.
Fuzzy mathematical programing is an efficient approach to solve multi-objective optimization problems, which models each objective as a fuzzy set whose membership function represents the degree of satisfaction of the objective.
Also, we identify the weight values which reflect the degree of satisfaction of each objective.
Satisfaction of this objective is important because if this rejected heat rate is not used, it corresponds to heat pollution.
Thus, the main new syntactic construct in the logic ATL* is a formula of the type \(\langle\!\langle C \rangle\!\rangle \varphi,\) intuitively saying "The coalition \(C\) has a collective strategy to guarantee the satisfaction of the objective \(\varphi\) on every play (path) enabled by that collective strategy of \(C\)".
For evaluation, we take a multi-pronged approach that combines implementation and prototyping as well as simulation and socio-economics into a suite of methods that aim at evaluating the overall satisfaction of our objectives.
The first expresses the satisfaction of design objectives, relating to physical behaviors of candidate design solutions.
The examined problem tackles the multi-stage manufacturing network design for single customised products, through satisfaction of multiple objectives.
The strategy is aimed at guaranteeing the satisfaction of comfort objectives, both thermal and lighting, by using the minimum amount of energy.
