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Recently, many researchers have shifted the focus from traditional optimization methods to the use of meta-heuristic approaches for handling this complexity.
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The used delivery algorithm is capable of handling this complexity relying on simple local information.
MapReduce framework is utilized efficiently for handling this operation.
Different solutions for handling this problem have been suggested.
As a result, there is a need for efficient algorithms that can handle this complexity.
The use of the P-graph framework as a synthesis toolset provides a strong mathematically proven fundament for handling the complexity of the synthesis problem.
A means for handling the complexity of these processes is through models, and software process modeling languages (SPMLs) are languages to express those processes.
Strategies for handling the complexity fall into one of two categories: (1) increasingly laborious trial-and-error exploration of the design space and mixture qualification process, or (2) systematic development of structure-property relationships, through curation of fundamental material component data and validated modeling based on fundamental scientific principles.
One way to handle this complexity is by devising satisficing measures for the relevant uncertainties.
Model-Based Systems Engineering (MBSE) methods are one approach to handle this complexity.
Bio-inspired techniques like self-organization are a key feature to handle this complexity.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com