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| Stochastic Mixed-Integer Programming× | MONTE-CARLO-SIMULATION× | |
|---|---|---|
| Tudományterület≠ | Szimuláció | Döntéshozatal |
| Módszercsalád≠ | Process / pipeline | MCDM |
| Keletkezés éve≠ | 1990s–2000s | 1949 |
| Megalkotó≠ | Birge, J. R.; Louveaux, F.; Sen, S. | Metropolis, N., Ulam, S. |
| Típus≠ | Stochastic optimization model | Robustness wrapper — Monte Carlo uncertainty propagation |
| Alapmű≠ | Birge, J. R., & Louveaux, F. (1997). Introduction to Stochastic Programming. Springer Series in Operations Research. New York: Springer. ISBN: 9780387982175 | Metropolis, N., Ulam, S. (1949). The Monte Carlo method. Journal of the American Statistical Association DOI ↗ |
| Alternatív nevek≠ | SMIP, Stochastic MIP, Mixed-Integer Stochastic Programming, SMILP | — |
| Kapcsolódó≠ | 5 | 0 |
| Összefoglaló≠ | 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. | 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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