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| 강건 선형 계획법× | 강건 혼합 정수 계획법× | |
|---|---|---|
| 분야 | 시뮬레이션 | 시뮬레이션 |
| 계열 | Process / pipeline | Process / pipeline |
| 기원 연도≠ | 1999–2004 | 1998–2004 |
| 창시자≠ | Ben-Tal, A. and Nemirovski, A.; further developed by Bertsimas, D. and Sim, M. | Ben-Tal & Nemirovski; Bertsimas & Sim |
| 유형≠ | Uncertainty-robust linear optimization | Deterministic robust reformulation of MIP under uncertainty |
| 원전 | Bertsimas, D., Sim, M. (2004). The price of robustness. Operations Research, 52(1), 35–53. DOI ↗ | Bertsimas, D., Sim, M. (2004). The price of robustness. Operations Research, 52(1), 35–53. DOI ↗ |
| 별칭 | RLP, Robust LP, Tractable Robust LP, Uncertainty-Set LP | RMIP, Robust MIP, Uncertain MIP, Robust MILP/MIQP |
| 관련≠ | 5 | 4 |
| 요약≠ | Robust Linear Programming (RLP) extends classical linear programming to handle uncertainty in problem data — cost coefficients, constraint coefficients, or right-hand sides — by requiring solutions to remain feasible and near-optimal across all realizations of uncertain parameters within a defined uncertainty set. It replaces probabilistic assumptions with worst-case guarantees, making it practical when distributional knowledge is limited. | Robust Mixed-Integer Programming (RMIP) combines mixed-integer programming with robust optimization to find solutions that remain feasible and near-optimal despite uncertain parameters. Instead of assuming fixed data, it protects decisions against adversarial or worst-case realizations of uncertain inputs, using an explicit uncertainty set to control the degree of conservatism while preserving the combinatorial structure of integer decisions. |
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