A rough set-based interval-valued intuitionistic fuzzy conceptual design decision approach with considering diverse customer preference distribution. (April 2021) - Jing, Liting; Yao, Jian; Gao, Fei; Li, Jiquan; Peng, Xiang; Jiang, Shaofei
Highlights: A conceptual scheme decision model considering diverse customer preference distribution. Identify implicit demand based on historical data to form the diverse preference. Intuitionistic fuzzy set can support decision under uncertainty in scheme selection. Rough set is used to preprocess the intuitionistic fuzzy set to form the IVIFS. Abstract: Conceptual design evaluation plays a crucial role in new product development (NPD) and determines the quality of downstream design activities. Currently, most existing methods focus on fuzzy quantitative the evaluation information of multi-objectives in conceptual schemes selection. However, the above process ignores the various customers' preferences for each scheme under the evaluation objective, causing inconsistent preference weights in the various schemes, which cannot guarantee the market value of the optimal scheme. Furthermore, the ambiguous attitude from experts in the early design stage is not well taken into account. To this end, a conceptual scheme decision model with considering diverse customer preference distribution based on interval-valued intuitionistic fuzzy set (IVIFS) is proposed. The model is divided into three parts. Firstly, the initial decision matrix of multi-experts concerning the qualitative and quantitative design attributes is constructed based on intuitionistic fuzzy sets, and then the IFS decision matrix with interval boundaries is formed by using rough set technology. Secondly, the mapping model of design attribute to customer preference is constructed, and then the demand preference strategy implied by design attribute is judged. Thirdly, based on the demand preference strategy, the preferences' weights for each scheme are calculated. Next, integrating the evaluation data with the same preference in the scheme, the comprehensive satisfaction of the scheme is obtained through IVIFS weighted aggregation operator, and then the optimal scheme is decided. Eventually, a case study of mobile phone form feature schemes is further employed to verify the proposed decision model, and results are sensitivity analyzed and compared.