A linguistic hesitant fuzzy group decision-making method for sustainable human-robot collaboration.

ABSTRACT: In intelligent manufacturing for complex products, the configuration and allocation of human-robot collaboration units (HRCUs) are of critical importance for enhancing production performance. To address the insufficient research on the impact of individual irrational behaviors and group-reference behaviors in HRCUs construction, a stable one-to-many human-robot-position matching decision-making (HRPMDM) method in hesitant fuzzy environments is proposed. Specifically, linguistic hesitant fuzzy sets (LHFSs) are adopted to characterize evaluators' dual uncertainties in linguistic term selection and membership degree assignment. Subsequently, the Cloud Model is adopted to quantitatively transform the LHFSs, thereby providing support for the proposed clustering algorithm based on cognitive simi

SUBMITTER: Zhang X 

PROVIDER: S-EPMC12510669 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature