Comparer des méthodes

	Programmation en nombres entiers pour scénarios politiques	Programmation Linéaire en Nombres Entiers Stochastique
Domaine	Simulation	Simulation
Famille	Process / pipeline	Process / pipeline
Année d'origine	1950s–1960s (scenario extension: 1990s onwards)	1955
Auteur d'origine	Operations research community (Dantzig, Gomory, and others)	Dantzig, G. B.; Beale, E. M. L.
Type	Discrete combinatorial optimization under scenario uncertainty	Optimization under uncertainty with discrete decisions
Source fondatrice	Birge, J. R., & Louveaux, F. (2011). Introduction to Stochastic Programming (2nd ed.). Springer. ISBN: 9781461402367	Birge, J. R., & Louveaux, F. (1997). Introduction to Stochastic Programming. Springer, New York. ISBN: 978-1-4614-0237-4
Alias	PSIP, scenario-based integer programming, policy-driven IP, scenario integer optimization	SIP, Stochastic IP, Integer Stochastic Programming, Mixed-Integer Stochastic Programming
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.	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.