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A Monte Carlo simulation based solution method is subsequently designed to solve the robust optimization model.
A Lagrangian relaxation based solution method is proposed to solve it.
A DoE-guided MOGA-II optimiser based solution method is proposed for locating a set of non-dominated solutions distributed along the Pareto frontier.
The comprehensive learning particle swarm optimization (CLPSO) based solution method is designed to search the optimal solutions of the proposed model.
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A local branching based solution method and a particle swarm optimization based solution method are developed for solving the model.
Artificial intelligence based solution methods are applied to obtain the optimal capacities using single and multi-objective functions.
In this paper an analytical-based solution method is developed.
A graph-based solution method is proposed with the objective of minimizing makespan.
A reduced-order model for the forced system is then determined by the usual NNM procedure, and an efficient Galerkin-based solution method is used to numerically construct the attendant invariant manifolds.
We detail our geometrically based solution method, in which material volume fluxes are computed systematically with a set of simple geometric tasks.
Fabrication of CuO nanograss based on a solution method is relatively simple and fast; thus, this result potentially provides a path toward cost-effective fabrication of a non-wettable surface for self-maintenance applications and a SERS substrate for sensing applications.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com