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<p>Operational risk assessment (ORA) practices have traditionally focused on assessing risks within a single operational objective, often overlooking the complexities and interdependencies present in modern operational environments. This study explores the challenges of ORA within a multi-objective and dynamic context, where risks need to be balanced and integrated across varied operational objectives and decision gates. Furthermore, the study addresses the challenges posed by incomplete knowledge and conflicting assessments, which are prevalent in real-world ORA scenarios. Utilizing evidence reasoning (ER) as a multi-criteria decision analysis framework, the paper presents a novel ER-ORA mathematical model to aggregate individual risk beliefs across various operational objectives and decision gates dynamically. The approach resolves conflicts between individual risk assessments and quantifies overall uncertainties and conflicts of the assessment. The ER-ORA model is validated through a dataset reflecting the aforementioned ORA challenges. The paper conclude on the ER-ORA’s functionality and propose future research directions.</p>

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A novel operational risk assessment model based on evidence reasoning for multi-objective and dynamic operational scenarios

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年4月9日

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  • Guicang Peng,
  • Jon T.ømmerås Selvik,
  • Eirik Bjorheim Abrahamsen,
  • Tore Markeset

摘要

Operational risk assessment (ORA) practices have traditionally focused on assessing risks within a single operational objective, often overlooking the complexities and interdependencies present in modern operational environments. This study explores the challenges of ORA within a multi-objective and dynamic context, where risks need to be balanced and integrated across varied operational objectives and decision gates. Furthermore, the study addresses the challenges posed by incomplete knowledge and conflicting assessments, which are prevalent in real-world ORA scenarios. Utilizing evidence reasoning (ER) as a multi-criteria decision analysis framework, the paper presents a novel ER-ORA mathematical model to aggregate individual risk beliefs across various operational objectives and decision gates dynamically. The approach resolves conflicts between individual risk assessments and quantifies overall uncertainties and conflicts of the assessment. The ER-ORA model is validated through a dataset reflecting the aforementioned ORA challenges. The paper conclude on the ER-ORA’s functionality and propose future research directions.