Comparer des méthodes

	Programmation en nombres entiers pour scénarios politiques	Programmation en nombres entiers robuste
Domaine	Simulation	Simulation
Famille	Process / pipeline	Process / pipeline
Année d'origine	1950s–1960s (scenario extension: 1990s onwards)	2003
Auteur d'origine	Operations research community (Dantzig, Gomory, and others)	Bertsimas, D. and Sim, M.
Type	Discrete combinatorial optimization under scenario uncertainty	Deterministic robust optimization with integer variables
Source fondatrice	Birge, J. R., & Louveaux, F. (2011). Introduction to Stochastic Programming (2nd ed.). Springer. ISBN: 9781461402367	Bertsimas, D., Sim, M. (2003). Robust discrete optimization and network flows. Mathematical Programming, 98(1-3), 49-71.
Alias	PSIP, scenario-based integer programming, policy-driven IP, scenario integer optimization	RIP, Robust IP, Robust Combinatorial Optimization, Integer Robust Optimization
Apparentées	2	6
Résumé	Policy Scenario Integer Programming (PSIP) solves an integer programming model — where some or all decision variables must take whole-number values — separately under each of several distinct policy scenarios, then compares objective values, feasibility, and solution structures to identify which policy environment leads to the best discrete allocation or assignment outcome.	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.