A novel cross-entropy framework for pattern design quality evaluation based on interval-valued intuitionistic fuzzy multi-attribute decision making

Abstract

Paper packaging is favored by manufacturers in various industries due to its advantages such as economy and environmental friendliness, and the application of paper packaging in various types of products is also becoming increasingly popular. However, with the growth of the national economy and the increasing purchasing power of the people, consumers not only have higher requirements for the functionality of products, but also have higher requirements for the visual effect of product packaging. Based on the potential consumption trend of “looking for things”, enterprises need to further strengthen the artistic design effect when using paper packaging, promoting the unity of practicality and aesthetics of paper packaging. Pattern design art, as a component of graphic design art, is mainly based on pattern modeling to convey artistic concepts and aesthetic temperament. Integrating pattern design art into the process of paper packaging design can effectively enhance the artistic value of paper packaging, thereby adding color to products. The pattern design quality evaluation is a classical multi-attribute decision making (MADM). In this paper, the MADM methodology is investigated based on cross-entropy (CE) method under interval-valued intuitionistic fuzzy sets (IVIFSs) for pattern design quality evaluation. First of all, this paper extends the CE method under the IVIFSs to propose the interval-valued intuitionistic fuzzy number cross-entropy (IVIFN-CE) method, it enlarges the application range of the CE method. Secondly, a new MADM model for pattern design quality evaluation based on IVIFN-CE algorithm is proposed. In this algorithm, attribute weight is calculated by CRITIC method which is a well-known objective method. At the end of this study, we noticed to a comparison between the proposed IVIFN-CE approach with other existing methods to verify the effectiveness of the algorithm.