Search

The energy consumption of mechanical products in China is enormous, and the energy utilization rate is low, which is increasingly receiving people's attention. Conducting product design for energy optimization is of great significance for improving energy utilization efficiency. The scheme design of a product is the key to achieving innovation in product design, and the evaluation and decision-making of the design scheme directly affect the results of the later stage of the design. Therefore, correctly evaluating and making reliable decisions on product design schemes that are oriented towards fuzzy decision optimization is an important aspect of product innovation conceptual design. The product modeling design quality evaluation is a multiple attribute group decision making (MAGDM) problems. Recently, the Combined Compromise Solution (CoCoSo) method and information entropy method has been employed to cope with MAGDM issues. The interval neutrosophic sets (INSs) are employed as a tool for portraying uncertain information during the product modeling design quality evaluation. In this paper, the CoCoSo method is designed for MAGDM under INSs. Then, the interval neutrosophic numbers CoCoSo (INN-CoCoSo) method based on the Hamming distance and Euclidean distance is built for MAGDM. The information Entropy method is employed to produce the weight information based on the Hamming distance and Euclidean distance under INNSs. Finally, a practical numerical example for product modeling design quality evaluation is supplied to show the INN-CoCoSo method. The main contributions of this paper are constructed: (1) This paper builds the novel MAGDM based on CoCoSo model under INSs; (2) The information Entropy method is employed to produce the weight information based on the Hamming distance and Euclidean distance under INNSs; (3) The new MAGDM method is proposed for product modeling design quality evaluation based on INN-CoCoSo. [ABSTRACT FROM AUTHOR]

In allusion to the previous various forecasting verification results of each similarity measurement, starting from formulas, the fact that there existed the same features between analog deviation and Hamming distance was analyzed and then proved. The limitations of analog deviation used in similarity forecast were discussed. The data from 96 different stations at 850 hPa in the East Asia during May 1 – 30, 2010 were utilized to make similarity selection experiments among several frequently used similarity measurements and the new one proposed by the authors of this essay. The result indicated that analog deviation and Hamming distance were very much similar, with over 80% selected samples being the same. Analog deviation had the biggest diference with similarity coefficient, with over 70% selected samples being diiferent. The new analog quantity was more similar to the related coefficient, with 60% selected samples being the same. Analog deviation and Hamming distance reflected how much the distances of the samples were similar to each other, while related coefficient and the new analog quantity reflected how much the shapes of the selected samples were similar to each other. [ABSTRACT FROM AUTHOR]