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A stochastic optimization framework is proposed for optimizing semi-batch reactive crystallizations.
The provided information are integrated in a stochastic optimization algorithm to select the optimal buffer allocation.
Due to the well-known curses of dimensionality most large-scale stochastic optimization problems cannot be solved to optimality.
The best log-likelihood map was identified using the "Annealing" command that optimized the maximum multipoint log-likelihood using a dedicated simulated annealing stochastic optimization algorithm.
Section 3 presents the stochastic optimization approach.
A stochastic optimization scheme is adopted.
The other algorithm is a stochastic optimization method.
The deterministic and stochastic optimization models can be derived through appropriate simplification of the fuzzy stochastic optimization model.
The total operating costs of the three optimization models, deterministic optimization, stochastic optimization and fuzzy stochastic optimization, are progressively reduced.
The stochastic optimization problem is solved backward in time.
Computational models, routinely based on stochastic optimization, support these decisions.
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