A Fuzzy Risk Assessment Approach Based on Z-Numbers for Enhancing Safety and Human-Robot Collaboration in Automotive Sector

Abstract

Industry 5.0 (I5.0) technologies introduce new workplace safety challenges, particularly in human-robot collaboration environments. While robots handle nonergonomic, repetitive, and hazardous tasks, existing risk assessment methods often fail to address the uncertainties in dynamic human-robot interactions. To bridge this gap, this study proposes a novel risk evaluation framework integrating fuzzy set theory with Z-numbers. The methodology integrates Delphi method for expert consensus on risk factors, decision-making trial and evaluation laboratory for causal relationships, analytic network process for importance considering interdependencies, and VIseKriterijumska Optimizacija I Kompromisno Resenje for ranking risks to prioritize mitigation actions. The methodology uniquely addresses hesitancy of experts' judgments and data imprecision through a systematic approach validated through a case study in one of Turkey's leading commercial vehicle manufacturers on a bus production line utilizing gantry-type industrial robots. A sensitivity analysis using VIKOR parameters further validates robustness. The Z-number framework overcomes traditional risk scores by differentiating scenarios yielding similar scores but distinct profiles, distinguishing low-probability/high-severity hazards from high-probability/low-severity ones, leading to nuanced prioritization.