Comparer des méthodes

	Programmation en nombres entiers robuste	Programmation Linéaire en Nombres Entiers Stochastique
Domaine	Simulation	Simulation
Famille	Process / pipeline	Process / pipeline
Année d'origine	2003	1955
Auteur d'origine	Bertsimas, D. and Sim, M.	Dantzig, G. B.; Beale, E. M. L.
Type	Deterministic robust optimization with integer variables	Optimization under uncertainty with discrete decisions
Source fondatrice	Bertsimas, D., Sim, M. (2003). Robust discrete optimization and network flows. Mathematical Programming, 98(1-3), 49-71.	Birge, J. R., & Louveaux, F. (1997). Introduction to Stochastic Programming. Springer, New York. ISBN: 978-1-4614-0237-4
Alias	RIP, Robust IP, Robust Combinatorial Optimization, Integer Robust Optimization	SIP, Stochastic IP, Integer Stochastic Programming, Mixed-Integer Stochastic Programming
Apparentées	6	6
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.	Stochastic Integer Programming (SIP) is an optimization framework that combines integer (discrete) decision variables with explicit probabilistic modeling of uncertainty. It seeks the best here-and-now decision that minimizes expected cost (or maximizes expected benefit) across a distribution of future scenarios, accounting for the fact that some decisions must be made before uncertainty is resolved.