방법 비교

	최적화 지원 사건수 분석	위험 기반 사건 트리 분석
분야	실험설계	실험설계
계열	Process / pipeline	Process / pipeline
기원 연도	1975 (ETA); optimization integration ~1990s–2000s	1975 (WASH-1400); risk-based integration formalized through 1980s–1990s PRA practice
창시자	Event tree analysis originated at the U.S. Nuclear Regulatory Commission (WASH-1400, 1975); optimization integration developed through risk engineering literature from the 1990s onward	Originated in nuclear industry (US Nuclear Regulatory Commission, WASH-1400 report); risk-based framing developed through probabilistic risk assessment practice
유형	Hybrid risk analysis and optimization method	Risk and reliability analysis technique
원전	Bedford, T., & Cooke, R. (2001). Probabilistic Risk Analysis: Foundations and Methods. Cambridge University Press. ISBN: 978-0521773194	Bedford, T., & Cooke, R. (2001). Probabilistic Risk Analysis: Foundations and Methods. Cambridge University Press. ISBN: 978-0521773201
별칭	OA-ETA, optimization-integrated ETA, optimization-enhanced event tree analysis, ETA with optimization	Risk-based ETA, probabilistic event tree analysis, consequence-probability event tree, risk-informed ETA
관련	5	4
요약	Optimization-assisted event tree analysis couples the structured probability logic of classical event tree analysis (ETA) with an optimization layer — typically mathematical programming or metaheuristic search — to identify the best combination of safety barriers, mitigation strategies, or resource allocations that minimizes risk or cost while satisfying engineering constraints. It is used in industrial risk engineering, nuclear safety, process industries, and infrastructure reliability.	Risk-based event tree analysis is a forward-looking, inductive risk assessment technique that models the consequences of an initiating event by tracing binary success/failure branches through safety barriers, then weights each outcome path by its probability to produce quantified risk estimates. Widely applied in nuclear, chemical process, aviation, and infrastructure safety engineering, it sits at the heart of probabilistic risk assessment (PRA) and supports both design decisions and regulatory compliance.