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| Robuste gemischt-ganzzahlige Programmierung× | Stochastische Gemischt-Ganzzahlige Programmierung× | |
|---|---|---|
| Fachgebiet | Simulation | Simulation |
| Familie | Process / pipeline | Process / pipeline |
| Entstehungsjahr≠ | 1998–2004 | 1990s–2000s |
| Urheber≠ | Ben-Tal & Nemirovski; Bertsimas & Sim | Birge, J. R.; Louveaux, F.; Sen, S. |
| Typ≠ | Deterministic robust reformulation of MIP under uncertainty | Stochastic optimization model |
| Wegweisende Quelle≠ | Bertsimas, D., Sim, M. (2004). The price of robustness. Operations Research, 52(1), 35–53. DOI ↗ | Birge, J. R., & Louveaux, F. (1997). Introduction to Stochastic Programming. Springer Series in Operations Research. New York: Springer. ISBN: 9780387982175 |
| Aliasnamen | RMIP, Robust MIP, Uncertain MIP, Robust MILP/MIQP | SMIP, Stochastic MIP, Mixed-Integer Stochastic Programming, SMILP |
| Verwandt≠ | 4 | 5 |
| Zusammenfassung≠ | Robust Mixed-Integer Programming (RMIP) combines mixed-integer programming with robust optimization to find solutions that remain feasible and near-optimal despite uncertain parameters. Instead of assuming fixed data, it protects decisions against adversarial or worst-case realizations of uncertain inputs, using an explicit uncertainty set to control the degree of conservatism while preserving the combinatorial structure of integer decisions. | 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. |
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