Compară metode

	Programare Liniară în Numere Întregi pentru Scenarii de Politici	Programare Stocastică cu Numere Întregi
Domeniu	Simulare	Simulare
Familie	Process / pipeline	Process / pipeline
Anul apariției	1950s–1960s (scenario extension: 1990s onwards)	1955
Autorul original	Operations research community (Dantzig, Gomory, and others)	Dantzig, G. B.; Beale, E. M. L.
Tip	Discrete combinatorial optimization under scenario uncertainty	Optimization under uncertainty with discrete decisions
Sursa seminală	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
Denumiri alternative	PSIP, scenario-based integer programming, policy-driven IP, scenario integer optimization	SIP, Stochastic IP, Integer Stochastic Programming, Mixed-Integer Stochastic Programming
Înrudite	2	6
Rezumat	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.