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The overall problem is formulated as a mixed integer dynamic multi-objective optimisation (MIDMO) problem.
The overall problem is formulated as a multi-objective mixed integer dynamic optimisation (MO-MIDO) problem.
The overall problem is formulated as a mixed integer dynamic optimisation problem and a solution procedure is proposed.
First, the development of an efficient hybrid evolutionary algorithm covering multi-objective mixed integer dynamic optimization problems is presented.
These formulations lead to a mixed integer dynamic optimization problems which are solved with mixed integer nonlinear programming methods after discretization of the underlying process dynamics.
Mixed integer dynamic programming is used to compute optimal mode transition strategies that protect vital loads and maximize the amount of non-vital loads that are supplied.
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The optimization of process dynamics, which is time varying, along with scheduling tasks is accomplished using rigorous mixed-integer dynamic optimization techniques.
This work aims at exploring two solution strategies to tackle the problem of simultaneous batch process synthesis and operational design, represented as a mixed-integer dynamic optimization (MIDO).
We consider strategies for integrated design and control through the robust and efficient solution of a mixed-integer dynamic optimization (MIDO) problem.
The resulting non-convex mixed-integer dynamic optimization problem is solved by means of a single-shooting method embedded in a branch & bound framework.
This paper treats an approach for configuring equipment and sequencing of complex batch distillation processes based on mixed-integer dynamic optimization.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com