Comparer des méthodes

	Programmation en nombres entiers robuste	Programmation Linéaire en Nombres Entiers Robuste
Domaine	Simulation	Simulation
Famille	Process / pipeline	Process / pipeline
Année d'origine	2003	1998–2004
Auteur d'origine	Bertsimas, D. and Sim, M.	Ben-Tal & Nemirovski; Bertsimas & Sim
Type	Deterministic robust optimization with integer variables	Deterministic robust reformulation of MIP under uncertainty
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	RMIP, Robust MIP, Uncertain MIP, Robust MILP/MIQP
Apparentées	6	4
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 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.