Analyzing energy consumption is an important task for a factory. In order to accomplish this task, most studies fit the relationship between energy consumption and product design features, process characteristics, or equipment types. However, the energy-saving effects of product yield learning are rarely considered. To bridge this gap, this study proposes a two-stage fuzzy approach to estimate the energy savings brought about by yield improvement. In the two-stage fuzzy approach, a fuzzy polynomial programming approach is first utilized to fit the yield-learning process of a product. Then, the relationship between monthly electricity consumption and increase in yield was fit to estimate the energy savings brought about by the improvement in yield. The actual case of a dynamic random-access memory factory was used to illustrate the applicability of the two-stage fuzzy approach. According to the experiment results, product yield learning can greatly reduce electricity consumption.

As a viable means to enhance the sustainability and competitiveness of aircraft manufacturing and maintenance, three-dimensional (3D) printing has been extensively used in the aircraft industry. However, due to the growing number of suitable 3D printers and the often-high prices of these 3D printers, aircraft manufacturers still face many obstacles in screening possible 3D printers. In addition, dependencies between criteria make it difficult for decision makers to properly assess their absolute priorities. Existing methods fail to address these issues. To solve this problem, this study proposes a nonlinear fuzzy geometric mean (FGM) and dependency-considered fuzzy vise kriterijumska optimizacija i kompromisno resenje (fuzzy VIKOR) approach. The first novel treatment is to design the nFGM method to ensure that the absolute priorities assigned to criteria are correct. Subsequently, in the dependency-considered fuzzy VIKOR, the dependencies between criteria are considered, and a realistic reference point is defined by measuring the distance from each 3D printer to it for proper evaluation. The nonlinear FGM and dependency-considered fuzzy VIKOR approach has been applied to assess and compare five 3D printers for manufacturing aircraft components.

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