Abstract
The design, planning, and implementation of intelligent manufacturing are mainly carried out from the perspectives of meeting the needs of mass customization, improving manufacturing capacity, and innovating business pattern currently. Environmental and social factors should be systematically integrated into the life cycle of intelligent manufacturing. In view of this, a green performance evaluation methodology of intelligent manufacturing driven by digital twin is proposed in this paper. Digital twin framework, which constructs the bidirectional mapping and real-time data interaction between physical entity and digital model, provides the green performance evaluation with a total factor virtual image of the whole life cycle to meet the monitoring and simulation requirements of the evaluation information source and demand. Driven by the digital twin framework, a novel hybrid MCDM model based on fuzzy rough-sets AHP, multistage weight synthesis, and PROMETHEE II is proposed as the methodology for the green performance evaluation of intelligent manufacturing. The model is tested and validated on a study of the green performance evaluation of remote operation and maintenance service project evaluation for an air conditioning enterprise. Testing demonstrates that the proposed hybrid model driven by digital twin can enable a stable and reasonable evaluation result. A sensitivity analysis was carried out by means of 27 scenarios, the results of which showed a high degree of stability.
Highlights
Guest Editor: Baogui Xin e design, planning, and implementation of intelligent manufacturing are mainly carried out from the perspectives of meeting the needs of mass customization, improving manufacturing capacity, and innovating business pattern currently
In order to make a final selection of the optimal alternatives, it is necessary to assess the reliability of the results obtained by the initial model. e most common means of assessing the reliability of the results is to compare them with other Multicriteria decision-making (MCDM) techniques. e discussion of the results is presented using the comparison of three MCDM methods (PROMETHEE II, TOPSIS, and VIKOR). ese methods were chosen because they have so far given stable and reliable results [27,28,29,30]
Ranking of the alternatives according to the models used in order to assess the reliability of the results shows that alternative 3 remained in the first place for the majority of the models (Tables 15–19). ere was a change in the ranking of alternative 3 using the FRSA-MSWS-TOPSIS model and FRSA-MSWS-TOPSIS model, whereby alternatives 3 and 2 changed places for the majority of the models
Summary
Guest Editor: Baogui Xin e design, planning, and implementation of intelligent manufacturing are mainly carried out from the perspectives of meeting the needs of mass customization, improving manufacturing capacity, and innovating business pattern currently. Cloud platform is used for promoting resource sharing and improving application efficiency of manufacturing system [5]; combining big data technology in the product life cycle to achieve sustainable intelligent manufacturing is proposed [6]; big data method was used for energy efficiency optimization [7], anomaly detection [8], and energy consumption monitoring [9] in intelligent manufacturing process; and through the analysis of relevant literature, it can be seen that there is no clear definition of green intelligent manufacturing in the current academic circle, and few research studies systematically integrated environmental and social factors into the design, planning, and implementation of intelligent manufacturing [10, 11]. There are many researches on performance evaluation of intelligent manufacturing, focusing on the following aspects
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