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The software generates solutions evaluated according to certain criteria, including mainly energy performance issues.
Through this workflow, a single CAD model generates solutions that are energy efficient and feasibly fabricated using digital techniques.
The proposed approach generates solutions very close to the ones obtained from nonconvex MINLP and is quite superior than simply resorting to recipes.
On the other hand, the algorithm generates solutions that have more than 80% of the met preferences, as defined by the users.
We conclude that the optimization technique proposed here generates solutions as those obtained using traditional (SCF) techniques, with the important advantage of reducing significantly the number of iterations needed to reach convergence.
We describe another linked tool which generates solutions to Prolog queries requesting advice on how a plan should be revised so as to comply with safety and efficacy requirements.
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When you start to generate solutions, you again want divergence.
They pose problems and challenges, then ask questions to enlist the entire team in generating solutions.
Furthermore, manufacturing issues properly limit the generated solutions.
Secondly, it facilitates knowledge extraction from the generated solutions.
MAI algorithm guarantees the feasibility of all the generated solutions.
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