Comparer des méthodes

	Programmation en nombres entiers robuste	Programmation Linéaire Robuste
Domaine	Simulation	Simulation
Famille	Process / pipeline	Process / pipeline
Année d'origine	2003	1999–2004
Auteur d'origine	Bertsimas, D. and Sim, M.	Ben-Tal, A. and Nemirovski, A.; further developed by Bertsimas, D. and Sim, M.
Type	Deterministic robust optimization with integer variables	Uncertainty-robust linear optimization
Source fondatrice	Bertsimas, D., Sim, M. (2003). Robust discrete optimization and network flows. Mathematical Programming, 98(1-3), 49-71.	Bertsimas, D., Sim, M. (2004). The price of robustness. Operations Research, 52(1), 35–53.
Alias	RIP, Robust IP, Robust Combinatorial Optimization, Integer Robust Optimization	RLP, Robust LP, Tractable Robust LP, Uncertainty-Set LP
Apparentées	6	5
Résumé	Robust Integer Programming (RIP) finds integer or binary solutions that remain feasible and near-optimal across all scenarios in a prescribed uncertainty set. Rather than assuming exact knowledge of data, RIP hedges against the worst-case realization of uncertain costs or constraint coefficients, delivering decisions that are guaranteed to perform well even when inputs deviate from their nominal values.	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.