Comparar métodos
Examine os métodos selecionados lado a lado; as linhas que diferem ficam destacadas.
| Programação Inteira Estocástica× | Programação Dinâmica Estocástica× | |
|---|---|---|
| Área | Simulação | Simulação |
| Família | Process / pipeline | Process / pipeline |
| Ano de origem≠ | 1990s–2000s | 1957 |
| Autor original≠ | Birge, J. R.; Louveaux, F.; Sen, S. | Bellman, R.; formalized for stochastic settings by Puterman, M. L. |
| Tipo≠ | Stochastic optimization model | Sequential optimization under uncertainty |
| Fonte seminal≠ | Birge, J. R., & Louveaux, F. (1997). Introduction to Stochastic Programming. Springer Series in Operations Research. New York: Springer. ISBN: 9780387982175 | Bellman, R. (1957). Dynamic Programming. Princeton University Press, Princeton, NJ. ISBN: 9780486428093 |
| Outros nomes | SMIP, Stochastic MIP, Mixed-Integer Stochastic Programming, SMILP | SDP, Markov Decision Process, MDP, Stochastic DP |
| Relacionados≠ | 5 | 6 |
| Resumo≠ | Stochastic Mixed-Integer Programming (SMIP) is an optimization framework that finds the best mix of binary, integer, and continuous decisions when key parameters — costs, demands, capacities — are uncertain and modeled as probability distributions over a set of scenarios. It extends classical MIP by embedding scenario trees or expected-value objectives that hedge against uncertainty while respecting combinatorial constraints. | Stochastic Dynamic Programming (SDP) is a mathematical optimization framework for sequential decision problems where outcomes are partly random. It extends Bellman's principle of optimality to stochastic environments, representing problems as Markov Decision Processes (MDPs) and computing optimal policies by solving recursive value equations over states and time periods. |
| ScholarGateConjunto de dados ↗ |
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