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The use of Bayesian methodologies for solving optimal experimental design problems has increased.
However, general methodologies for solving this class of design problems are not easily encountered in the literature because of the complexity of the resultant multiobjective problems.
In recent years there has been a significant growth of interest in exploiting the principles of biological processes to create powerful methodologies for solving computational problems.
Though there have existed some methodologies for solving this problem, such as genetic algorithms, gradient descent algorithms and neural networks, it is hard to say which one is more appropriate and more feasible.
Such parallel computation has become very important in many fields of engineering and it allows using completely different methodologies for solving the problem in each individual part, and even if this is not used, it allows one very much to increase the computer power by having separate operations going on simultaneously.
In this section, solution methodologies for solving mathematical models are presented specifically supply chain network models which were proposed by recent researchers.
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Design thinking is a methodology for solving complex problems that's used by designers in developing new products.
They'll need personnel procedures and practices that enable empowerment, access to detailed business information, and the quality process as a methodology for solving business problems.
This paper proposes a multi-objective formulation of MRAOPs and a methodology for solving such problems.
This paper presents a novel methodology for solving multi-objective Computer Aided Molecular Design (CAMD) problems.
Artificial raindrop algorithm (ARA) has been shown to be an effective methodology for solving optimization problems over continuous space.
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