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Stochastické lineárne programovanie×Simulácia Monte Carlo×
OdborSimuláciaRozhodovanie
RodinaProcess / pipelineMCDM
Rok vzniku19551949
TvorcaGeorge B. DantzigMetropolis, N., Ulam, S.
TypStochastic optimization modelRobustness wrapper — Monte Carlo uncertainty propagation
Pôvodný zdrojDantzig, 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. link ↗Metropolis, N., Ulam, S. (1949). The Monte Carlo method. Journal of the American Statistical Association DOI ↗
Ďalšie názvySLP, Stochastic LP, Linear Programming under Uncertainty, Two-Stage SLP
Príbuzné50
ZhrnutieStochastic 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.MONTE-CARLO-SIMULATION (Monte Carlo Simulation — Stochastic uncertainty propagation through MCDM model) is a ranking multi-criteria decision-making (MCDM) method introduced by Metropolis, N., Ulam, S. in 1949. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.
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ScholarGatePorovnať metódy: Stochastic Linear Programming · MONTE-CARLO-SIMULATION. Získané 2026-06-15 z https://scholargate.app/sk/compare