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| 심층 불확실성 하에서의 최악의 경우 및 최소 최대 후회 평가를 포함한 강건 시나리오 분석× | 강건 최적화× | |
|---|---|---|
| 분야≠ | 시뮬레이션 | 최적화 |
| 계열 | Process / pipeline | Process / pipeline |
| 기원 연도≠ | 1950 (foundations); 2003 (modern RDM formulation) | 1970s theoretical roots; modern tractable form from late 1990s–2004 |
| 창시자≠ | Wald, A. (minimax foundation); Lempert et al. (RDM framework) | Ben-Tal, El Ghaoui & Nemirovski (seminal book, 2009); Bertsimas & Sim (tractable polyhedral formulation, 2004) |
| 유형≠ | Scenario-based robustness evaluation | Mathematical programming framework |
| 원전≠ | Wald, A. (1950). Statistical Decision Functions. Wiley, New York. link ↗ | Ben-Tal, A., El Ghaoui, L. & Nemirovski, A. (2009). Robust Optimization. Princeton University Press. ISBN: 9780691143682 |
| 별칭≠ | RSA, Robust Scenario Planning, Worst-Case Scenario Analysis, Minimax Regret Scenario Analysis | minimax optimization, worst-case optimization, Gürbüz Optimizasyon (Robust Optimization) |
| 관련 | 5 | 5 |
| 요약≠ | Robust Scenario Analysis evaluates a set of candidate strategies across a structured collection of plausible future scenarios and selects the strategy that performs acceptably well — or best in the worst case — regardless of which scenario materializes. It merges scenario planning with robustness criteria such as maximin, minimax regret, or satisficing to support decisions under deep, irreducible uncertainty. | Robust optimization is a mathematical programming framework, formalised by Ben-Tal and Nemirovski in the late 1990s and made broadly tractable by Bertsimas and Sim (2004), that finds decisions guaranteed to perform acceptably under every scenario within a predefined uncertainty set — rather than assuming parameter values are known exactly. Instead of optimising for a single expected outcome, it minimises the worst-case objective across all plausible realisations of uncertain data. |
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