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Therefore, this uncertainty can be accounted for within the optimization problem, and thus the process optimal conditions are robust against the modelling uncertainty.
To overcome this challenge, an approximation for the posterior distribution is employed within the optimization problem to facilitate the identification of the optimal sensor locations using the simulated annealing algorithm.
In Section 3, we investigate the pairing problem by including the pairing parameters within the optimization problem studied in Section 2.
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Thus, there is a need to define additional constraints within which the optimization problem must be solved.
Within each iteration, the optimization problem is transformed equivalently into two subproblems, i.e., power allocation subproblem of each SU-spectrum pair and spectrum handoff subproblem for all the SUs.
We solve both phases of the optimization problem to within 5% of optimality and demonstrate that solutions from the minute-level model more closely match the load, more closely capture battery and generator behavior, and provide fuel savings from a few percent to 30% over that provided by the hour-level model for the tested scenarios.
Hence, we can expect that there are good solutions to the optimization problem within the LH even if none of the initial 19 parameter vectors are particularly good.
To further clarify this issue, we consider a grid of values for k2 and k5 in the region defined by constraints 4 ≤ k2 ≤ 5 and 0.2 ≤ k5 ≤ 0.8, and solve the optimization problem within each cell applying BARON to the SC model, and our OA to the rGMA model.
The optimization problem is formulated within the Generalized Disjunctive Programming (GDP) framework and the solution of the reformulated MINLP problem is approached with a decomposition strategy based on the Outer-Approximation algorithm, where NLP subproblems are solved with the derivative free optimizer belonging to the BzzMath library, and MILP master problems are solved with CPLEX/GAMS.
Outer approximation algorithm within gPROMS software is used to solve the optimization problem.
The optimization problem is implemented within the MATLAB environment and executed using the MOSEK optimization toolbox with standard settings for mixed integer 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