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분야시뮬레이션시뮬레이션
계열Process / pipelineProcess / pipeline
기원 연도19471999–2004
창시자George B. DantzigBen-Tal, A. and Nemirovski, A.; further developed by Bertsimas, D. and Sim, M.
유형Deterministic mathematical optimizationUncertainty-robust linear optimization
원전Dantzig, G. B. (1963). Linear Programming and Extensions. Princeton University Press, Princeton, NJ. ISBN: 9780691059136Bertsimas, D., Sim, M. (2004). The price of robustness. Operations Research, 52(1), 35–53. DOI ↗
별칭Classical LP, Deterministic LP, DLP, Linear OptimizationRLP, Robust LP, Tractable Robust LP, Uncertainty-Set LP
관련55
요약Deterministic Linear Programming (DLP) is the classical form of linear programming in which all objective function coefficients, constraint coefficients, and right-hand-side values are known with certainty. It finds the optimal allocation of resources to maximize or minimize a linear objective subject to linear constraints, providing an exact, reproducible solution under fixed, certain data.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.
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