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At first, all the probabilistic constraints are transformed into deterministic constraints using stochastic programming approach.
The deterministic constraints restrict the capacity of any arbitrary cooling channel between two extreme limits.
In the SLDM-IDE, all of the probabilistic constraints are converted to the approximate deterministic constraints.
We formulate these features as deterministic constraints and probabilistic constraints to prune false positives and effectively prioritize remaining faults.
The simulation models include combinations of real variables, integer variables, non-numeric variables, deterministic constraints, and stochastic constraints.
Fortunately, as shown in [24, 25], the above probabilistically constrained problem can be solved as an integer programming (IP) problem with deterministic constraints.
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To solve this problem, we reformulate it into an MBIP problem with a deterministic constraint on the outage upper bound.
Principle of the sequential strategy is to construct an equivalent deterministic constraint instead of the original probabilistic one, which is realized by using the locally exponential approximation.
Only deterministic constraint functions are considered herein (i.e. they do not depend on the outcome of a reliability analysis).
Compared to the basic deterministic-based optimization problem, a RBDO problem considers additional non-deterministic constraint functions, while the RDO yields a design with a state of robustness, so that its performance is the least sensitive to the variability of the uncertain parameters.
The model error is also used to convert the deterministic process constraints into stochastic constraints which are respected with a given accuracy.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com