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Programació Enter Robusta×Programació estocàstica entera×
CampSimulacióSimulació
FamíliaProcess / pipelineProcess / pipeline
Any d'origen20031955
Autor originalBertsimas, D. and Sim, M.Dantzig, G. B.; Beale, E. M. L.
TipusDeterministic robust optimization with integer variablesOptimization under uncertainty with discrete decisions
Font seminalBertsimas, D., Sim, M. (2003). Robust discrete optimization and network flows. Mathematical Programming, 98(1-3), 49-71. DOI ↗Birge, J. R., & Louveaux, F. (1997). Introduction to Stochastic Programming. Springer, New York. ISBN: 978-1-4614-0237-4
ÀliesRIP, Robust IP, Robust Combinatorial Optimization, Integer Robust OptimizationSIP, Stochastic IP, Integer Stochastic Programming, Mixed-Integer Stochastic Programming
Relacionats66
ResumRobust 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.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.
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ScholarGateCompara mètodes: Robust Integer Programming · Stochastic Integer Programming. Recuperat el 2026-06-15 de https://scholargate.app/ca/compare