Research Information System
Engineering Applications of Artificial Intelligence, vol.163, 2026 (SCI-Expanded, Scopus)
The advancement of Industry 5.0 brings unprecedented challenges and complexities in transportation and mobility systems, demanding robust and adaptive risk assessment frameworks. Traditional rough set theory examines how decision-makers respond to risks based on psychological behaviors and preferences, while three-way decision (3-WD) theory provides a structured approach to managing uncertainty through acceptance, rejection, or deferment actions. However, current methods often struggle with incomplete, multiscale data and vague failure information. In response, this study introduces a novel data-driven 3-WD model that integrates rough set theory (RST) with circular Pythagorean fuzzy sets (Cir-PyFSs) to handle missing utility values and ambiguous information in high-stakes environments. Cir-PyFSs capture membership, non-membership, and radius information, offering enhanced flexibility and precision in modeling uncertainty. The proposed model incorporates decision-theoretic rough sets (DTRSs) with a relative loss function framework, effectively addressing unexpected uncertainties and cost-sensitive decisions. To demonstrate its applicability, we present a multi-attribute decision-making (MADM) model based on newly developed operators within Cir-PyFSs settings. Our approach is applied to a real-world transportation and mobility scenario, evaluating the risks and impacts associated with Industry 5.0 adoption. The findings reveal that the proposed model provides a powerful and practical tool for advanced risk assessment, enabling Industry leaders and policymakers to make informed, resilient decisions in rapidly evolving industrial landscapes.