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An example of problem classes comes from the semiconductor industry.
We present planning algorithms for these problem classes, and prove that they are correct and complete.
Thus, generality does not necessarily go against efficiency for these problem classes.
It is an efficient tool for many continuous multimodal and multidimensional problem classes.
This study demonstrates that problem classes and regions of the phase transition previously thought to be easy can sometimes be orders of magnitude more difficult than the worst problems in problem classes and regions of the phase transition considered hard.
Among the predominant topics treated in the literature, we focus on two different, but related problem classes.
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"Particle-caused defects" was thus a problem class.
Scenario-based optimization is a problem class often occurring in finance, planning and control.
The results demonstrated the potential of the inference system to this problem class.
This problem class has applicability in areas such as production and logistics planning and financial engineering.
However, this prediction is associated with a large variance and depends on the somewhat arbitrary choice of the problem class.
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