While optimization can play a lead role in increasing the efficiency of production planning (necessary for automated manufacturing systems), it can also be used to optimize the products themselves.
Let us assume that a company sells a certain product, be it an iron work piece or an optical system. The features of this product that are relevant for production could comprise production or material costs as well as quality metrics, such as stability or an alignment error measures. Assume further that we are able to compute such features from the building plan or design of such a work piece, either directly by using equations, via simulations, or by using some dedicated design software. In this scenario, the design parameters of the work piece, e.g., the shape, form, location, and number of lenses of an optical system or the shape and components of the alloy used to make a metal work piece can be subject to optimization: An optimization procedure can start with an existing design and step-by-step modify its parameters in order to improve the desired metrics (quality, costs, etc). This process can even incorporate measures of production uncertainties or design robustness.
As a result, cheaper and better products can be designed.
The ability to optimize product features can be another integral component for on-demand and highly-customizable production, as required by new concepts such as Industry 4.0 and Made in China 2025 [中国制造2025] and may directly be integrated with production planning and logistics.
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Indexing: EI:20124815716038, WOS:000311506900029, SCI, 1区
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Indexing: EI:20130916069460, WOS:000306767200005, SCI, 2区, CCF-B类
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Indexing: EI:20113014172389, WOS:000293548900010, SCI, Google Scholar, 2区/1区, CCF-B类
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Indexing: EI:20101412821019, WOS:000277471400005, SCI, Google Scholar, 2区/1区, CCF-B类