A Hybrid MCDM Framework for Industrial Robot Appearance Evaluation Based on BWM-CRITIC Combined Weighting and VIKOR Ranking

Abstract

To address the evaluation and selection of industrial robot appearances, this study constructs a hybrid multi-criteria decision-making (MCDM) framework. Initial evaluation criteria are derived from users’ perceptual impressions, systematically organized using the KJ method under the paradigm of Kansei Engineering, and hierarchically structured based on Donald Norman’s three-level theory of emotional design. To overcome the limitations of single weighting techniques, this study adopts a dual-perspective weighting strategy: subjective weights reflecting expert judgments are obtained via the Best-Worst Method (BWM), while objective weights are generated using an improved CRITIC method integrated with information entropy, which accounts for the contrast intensity, conflict, and information utility inherent in the evaluation data. The two sets of weights are further fused into a final combined weight vector using the maximizing deviation method. Finally, the VIKOR ranking method is employed to comprehensively evaluate and rank four representative industrial robot design schemes. The validation results demonstrate that the proposed BWM-CRITIC combined weighting approach, coupled with VIKOR ranking, effectively balances subjective cognitive judgments and objective mathematical characteristics. Therefore, the proposed framework provides a robust and implementable tool for the screening and evaluation of industrial robot appearance designs, with potential extensions to other product form evaluation domains.

Keywords: Industrial robot, Appearance evaluation, Multi-criteria decision-making (MCDM), Kansei engineering, Combined weighting, VIKOR method

DOI: 10.54941/ahfe1008156