Design concept evaluation based on rough number and information entropy theory

Concept evaluation at the early phase of product development plays a crucial role in new product development. It determines the direction of the subsequent design activities. However, the evaluation information at this stage mainly comes from experts' judgments, which is subjective and imprecise. How to manage the subjectivity to reduce the evaluation bias is a big challenge in design concept evaluation. This paper proposes a comprehensive evaluation method which combines information entropy theory and rough number. Rough number is first presented to aggregate individual judgments and priorities and to manipulate the vagueness under a group decision-making environment. A rough number based information entropy method is proposed to determine the relative weights of evaluation criteria. The composite performance values based on rough number are then calculated to rank the candidate design concepts. The results from a practical case study on the concept evaluation of an industrial robot design show that the integrated evaluation model can effectively strengthen the objectivity across the decision-making processes

Evaluating biological inspiration for biologically inspired design: an integrated DEMATEL-MAIRCA based on fuzzy rough numbers

Biological inspiration evaluation has been widely acknowledged as one of the most important phases in biologically inspired design (BID) as it substantially determines the direction of the following-up design activities. However, it is inherently an interdisciplinary assessment, which includes both the engineering domain and the biological systems. Due to the lack of knowledge at the early stage of product design, the risk assessments mainly depend on experts' subjective judgments, which values are vague, imprecise, and even inconsistent. How to objectively evaluate the biological inspiration under such uncertain and interdisciplinary scenarios remains an open issue. To bridge such gaps, this study proposes a fuzzy rough number extended multi-criteria group decision-making (MCGDM) to evaluate the biological inspiration for BID. A fuzzy rough number is introduced to represent the individual decision maker's risk assessment and aggregate respective evaluation values within the decision-making group. A fuzzy rough number extended decision-making trial and evaluation laboratory is presented to determine the criteria weights and a fuzzy rough number extended multi-attribute ideal real comparative analysis is proposed to rank the candidate biological inspirations. Experimental results and comparative analysis validate the superiority of the proposed MCGDM in handling the subjectivity and uncertainty in biological inspiration evaluation.This study was partly supported by the Shanghai Pujiang Program (Grant/Award No. 20PJ1406600)

A fuzzy rough number-based AHP-TOPSIS for design concept evaluation under uncertain environments

Design concept evaluation in the early phase of product design plays a crucial role in new product development as it considerably determines the direction of subsequent design activities. However, it is a process involving uncertainty and subjectivity. The evaluation information mainly relies on expert's subjective judgment, which is imprecise and uncertain. How to effectively and objectively evaluate the design concept under such subjective and uncertain environments remains an open question. To fill this gap, this paper proposes a fuzzy rough number-enhanced group decision-making framework for design concept evaluation by integrating a fuzzy rough number-based AHP (analytic hierarchy process) and a fuzzy rough number-based TOPSIS (technique for order preference by similarity to ideal solution). First of all, a fuzzy rough number is presented to aggregate personal risk assessment information and to manipulate the uncertainty and subjectivity during the decision-making. Then a fuzzy rough number-based AHP is developed to determine the criteria weights. A fuzzy rough number-based TOPSIS is proposed to conduct the alternative ranking. A practical case study is put forward to illustrate the applicability of the proposed decision-making framework. Experimental results and comparative studies demonstrate the superiority of the fuzzy rough number-based method in dealing with the uncertainty and subjectivity in design concept evaluation under group decision-making environment.This work is partly supported by the National Natural Science Foundation of China (No. 51775332)