Σύγκριση μεθόδων

	Στιβαρή Γραμμική Προγραμματισμός	Στοχαστικός Γραμμικός Προγραμματισμός
Πεδίο	Προσομοίωση	Προσομοίωση
Οικογένεια	Process / pipeline	Process / pipeline
Έτος προέλευσης	1999–2004	1955
Δημιουργός	Ben-Tal, A. and Nemirovski, A.; further developed by Bertsimas, D. and Sim, M.	George B. Dantzig
Τύπος	Uncertainty-robust linear optimization	Stochastic optimization model
Θεμελιώδης πηγή	Bertsimas, D., Sim, M. (2004). The price of robustness. Operations Research, 52(1), 35–53.	Dantzig, G. B., & Madansky, A. (1961). On the solution of two-stage linear programs under uncertainty. Proceedings of the Fourth Berkeley Symposium on Mathematical Statistics and Probability, 1, 165–176.
Εναλλακτικές ονομασίες	RLP, Robust LP, Tractable Robust LP, Uncertainty-Set LP	SLP, Stochastic LP, Linear Programming under Uncertainty, Two-Stage SLP
Συναφείς	5	5
Σύνοψη	Robust Linear Programming (RLP) extends classical linear programming to handle uncertainty in problem data — cost coefficients, constraint coefficients, or right-hand sides — by requiring solutions to remain feasible and near-optimal across all realizations of uncertain parameters within a defined uncertainty set. It replaces probabilistic assumptions with worst-case guarantees, making it practical when distributional knowledge is limited.	Stochastic Linear Programming (SLP) extends classical linear programming to settings where some model parameters — costs, demands, resource availability — are uncertain and modeled as random variables. By optimizing expected costs over a probability distribution of scenarios, SLP produces decisions that remain feasible and near-optimal across a range of possible futures rather than for a single assumed state of the world.