Comparar métodos
Examine os métodos selecionados lado a lado; as linhas que diferem ficam destacadas.
| Programação por Metas Estocástica× | Programação Inteira Estocástica× | |
|---|---|---|
| Área | Simulação | Simulação |
| Família | Process / pipeline | Process / pipeline |
| Ano de origem≠ | 1968 | 1955 |
| Autor original≠ | Contini, B. (building on Charnes & Cooper's chance-constrained programming) | Dantzig, G. B.; Beale, E. M. L. |
| Tipo≠ | Stochastic multi-goal optimization | Optimization under uncertainty with discrete decisions |
| Fonte seminal≠ | Contini, B. (1968). A stochastic approach to goal programming. Operations Research, 16(3), 576–586. DOI ↗ | Birge, J. R., & Louveaux, F. (1997). Introduction to Stochastic Programming. Springer, New York. ISBN: 978-1-4614-0237-4 |
| Outros nomes | SGP, Stochastic GP, Chance-Constrained Goal Programming, Probabilistic Goal Programming | SIP, Stochastic IP, Integer Stochastic Programming, Mixed-Integer Stochastic Programming |
| Relacionados | 6 | 6 |
| Resumo≠ | Stochastic Goal Programming (SGP) extends classical goal programming to handle uncertainty in goal targets, constraint coefficients, or right-hand-side parameters. By incorporating probabilistic constraints and stochastic objective components, it finds solutions that satisfy multiple goals at acceptable probability levels, making it suitable for decision problems where data are inherently uncertain or variable. | 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. |
| ScholarGateConjunto de dados ↗ |
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