Comparer des méthodes

	Programmation Linéaire en Nombres Entiers Robuste	Programmation Linéaire en Nombres Entiers
Domaine	Simulation	Simulation
Famille	Process / pipeline	Process / pipeline
Année d'origine	1998–2004	1958–1960
Auteur d'origine	Ben-Tal & Nemirovski; Bertsimas & Sim	Ralph Gomory (branch-and-bound cuts, 1958); Land & Doig (branch-and-bound, 1960)
Type	Deterministic robust reformulation of MIP under uncertainty	Mathematical optimization
Source fondatrice	Bertsimas, D., Sim, M. (2004). The price of robustness. Operations Research, 52(1), 35–53.	Nemhauser, G. L., Wolsey, L. A. (1988). Integer and Combinatorial Optimization. Wiley-Interscience, New York. ISBN: 9780471359432
Alias	RMIP, Robust MIP, Uncertain MIP, Robust MILP/MIQP	MIP, Mixed-Integer Linear Programming, MILP, Integer Programming
Apparentées	4	6
Résumé	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.	Mixed-Integer Programming (MIP) is a mathematical optimization framework in which some decision variables must take integer values while others may be continuous. It generalizes linear programming and is widely used in operations research, logistics, scheduling, resource allocation, and engineering design, where indivisibility constraints — such as yes/no decisions or whole-unit quantities — arise naturally.