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Dynamic optimization problems present great challenges to the research community because their parameters are either revealed or changed during the course of an ongoing optimization process.
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The process is done dynamically, due to ongoing optimization along with simulation running.
Within the ongoing H2020 AGILE research, an aircraft MDO (Multidisciplinary Design Optimization) process is setting up linking several design tools and, above all, competences together.
Figure 5 Redundancy optimization process.
In addition to cost savings, the company expects to derive additional benefits from ongoing optimization efforts.
Along with ongoing optimization, it makes life more interesting, not to mention more rewarding.
The machine learning automatically and continuously feeds a closed loop experimentation cycle that allows for ongoing optimization of marketing interactions executed on a per customer and context basis.
Accordingly solidity and roundness increased during the optimization process.
Since the optimization process is stochastic, the resulting partition varies between realizations of the process.
This optimization process involves inverse treatment planning.
The optimization process is as follows.
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