Linguistic Z-numbers and cloud model weighted ranking technology and its application in concept evaluation of information axiom

Concept design evaluation is the most critical part of product design, which determines the direction of the subsequent design stage. Due to the various uncertainties inherent in decision-maker (DM)'s linguistic evaluation, such as fuzziness and randomness, how to deal with these uncertainties reasonably is a challenge. Only using a single uncertainty treatment method may not be enough to select an appropriate conceptual design. Therefore, this paper aims to develop an extended information axiom concept design evaluation model combining linguistic Z-numbers (LZNs) and axiomatic design methods to deal with the uncertainty in concept design evaluation. Specifically, the LZNs are used to express DM's subjective evaluation information and the reliability of the evaluation result. In the evaluation process, the uncertainty caused by linguistic variables is represented by the cloud model. Moreover, the compatibility weights of concepts are derived objectively based on the compatibility matrix. Finally, the design combination with the minimum information content is obtained by using the extended information axiom. The feasibility and applicability of this method are verified by an evaluation example of ink pen concept design. Compared with the existing methods, the proposed method considers more uncertainty and is more in line with the actual situation.