## 2 Literature Review
### Existing Modeling Approaches  
Mathematical models enable the simulation and prediction of multiple scenarios and stressors in diverse, complex fields ([PERSON] et al., 2019). By meticulously quantifying stress factors, potential failures, and their consequences, these models empower decision makers to proactively identify vulnerabilities and allocate safety investments more effectively ([PERSON] et al., 2019). Additionally, mathematical modeling excels in handling unpredictability in high-risk environments, enabling an assessment of the effects of varying conditions and unforeseen events ([PERSON] et al., 2019). Consequently, it aids in safeguarding lives and assets but, above all, fosters a proactive approach to risk mitigation for critical systems, making them resilient in the face of the unknown. The literature proposes many models and techniques capable of solving complex problems in the risk management domain while considering uncertainty. [PERSON] and [PERSON] (2019) discuss Circular Supply Chains (CSCs) and their role in promoting material circularity. Their study emphasizes the lack of attention to risk and uncertainty management within CSCs, proposing a Delphi study with experts from various countries to identify risks. The outcome is to offer strategies like collaboration and circular product design to manage these risks while accounting for contingency factors impacting CSCs. [PERSON] et al. (2019) develop a qualitative approach to managing emerging risks in industrial settings, focusing on uncertainty as a critical factor. The authors introduce a theoretical framework that integrates uncertainty, knowledge, and understanding by proposing a classification scheme for emerging risks. The approach is applied to case studies involving various emerging risks. [PERSON] et al. (2019) conduct a comprehensive review of research on Occupational Health and Safety Risk Assessment (OHSRA). The review aims to identify and evaluate models and approaches for assessing and prioritizing the risk of occupational hazards in the workplace. The study reviews 88 publications from 2002 to 2022, categorizing them into seven groups based on OHSRA models and analyzing their risk criteria, weighting methods, and assessment approaches. Additionally, it conducts a bibliometric analysis to identify research trends and hotspots in the field, providing a valuable resource for scholars and practitioners involved in OHSRA and offering insights into current developments and future research directions. [PERSON] et al. (2019) focus on the occupational risk assessment of magnetic nanoparticles used in such critical processes as medical imaging by employing a software-based Decision Support System (DSS) following regulatory requirements and making use of hazard and exposure assessment models. The study performs probabilistic risk characterizations, highlighting the scenario that poses non-negligible risks to workers and establishing specific management measures. [PERSON] et al. [28] introduce a new method for improving occupational accident prediction using machine learning. The developed approach tackles challenges like data uncertainty and unstructured information by combining rough set-based predictive and topic modeling. The method also incorporates an optimized machine learning model and rule reliability assessment while leading experiments on various datasets, including real-life occupational safety data. [PERSON] et al. [29] address the fatigue problem in aviation, highlighting the lack of a standardized protocol for assessing and managing it across the industry. The authors present a framework supported by real-world data and various measurements to monitor aircrew fatigue and performance to determine safe crew configurations for commercial airline operations. The study confirms the feasibility of this framework and its ability to provide valuable insights into fatigue and work performance, facilitating the development of safety recommendations. [PERSON] et al.