방법 비교
선택한 방법을 나란히 검토하세요. 서로 다른 행은 강조 표시됩니다.
| 다중 목표 시나리오 분석× | 확률적 시나리오 분석× | |
|---|---|---|
| 분야 | 시뮬레이션 | 시뮬레이션 |
| 계열 | Process / pipeline | Process / pipeline |
| 기원 연도≠ | 2013 (integrated framework); scenario analysis roots: 1967 | 1955–1980s |
| 창시자≠ | Stewart, French & Rios (integration formalized); scenario analysis roots: Kahn & Wiener (1967) | Dantzig, G. B.; Birge, J. R.; and others in stochastic programming tradition |
| 유형≠ | Structured qualitative-quantitative hybrid | Probabilistic scenario enumeration and evaluation |
| 원전≠ | Stewart, T. J., French, S., & Rios, J. (2013). Integrating multicriteria decision analysis and scenario planning: Review and extension. Omega, 41(4), 679-688. DOI ↗ | Birge, J. R., Louveaux, F. (2011). Introduction to Stochastic Programming (2nd ed.). Springer. ISBN: 9781461402374 |
| 별칭 | MOSA, Multi-criteria scenario analysis, Multi-objective futures analysis, MO-scenario analysis | Probabilistic Scenario Analysis, SSA, Stochastic What-If Analysis, Monte Carlo Scenario Analysis |
| 관련 | 4 | 4 |
| 요약≠ | Multi-objective Scenario Analysis (MOSA) is a structured method that constructs a set of plausible future scenarios and evaluates each scenario against multiple competing objectives or criteria. By making trade-offs explicit across objectives and across possible futures, it supports strategic decisions where uncertainty about the future and conflicts between goals co-exist. It is widely applied in energy planning, climate adaptation, public policy, and corporate strategy. | Stochastic Scenario Analysis evaluates a system or decision across multiple explicitly defined scenarios, each assigned a probability of occurrence. Unlike deterministic scenario analysis, it propagates uncertainty through probability distributions and computes expected outcomes, variance, and risk metrics across the scenario space, giving decision-makers a structured view of what could happen and how likely each outcome is. |
| ScholarGate데이터셋 ↗ |
|
|