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| Robuste gemischt-ganzzahlige Programmierung× | Mixed-Integer Programming× | |
|---|---|---|
| Fachgebiet | Simulation | Simulation |
| Familie | Process / pipeline | Process / pipeline |
| Entstehungsjahr≠ | 1998–2004 | 1958–1960 |
| Urheber≠ | Ben-Tal & Nemirovski; Bertsimas & Sim | Ralph Gomory (branch-and-bound cuts, 1958); Land & Doig (branch-and-bound, 1960) |
| Typ≠ | Deterministic robust reformulation of MIP under uncertainty | Mathematical optimization |
| Wegweisende Quelle≠ | Bertsimas, D., Sim, M. (2004). The price of robustness. Operations Research, 52(1), 35–53. DOI ↗ | Nemhauser, G. L., Wolsey, L. A. (1988). Integer and Combinatorial Optimization. Wiley-Interscience, New York. ISBN: 9780471359432 |
| Aliasnamen | RMIP, Robust MIP, Uncertain MIP, Robust MILP/MIQP | MIP, Mixed-Integer Linear Programming, MILP, Integer Programming |
| Verwandt≠ | 4 | 6 |
| 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. | Mixed-Integer Programming (MIP) is a mathematical optimization framework in which some decision variables must take integer values while others may be continuous. It generalizes linear programming and is widely used in operations research, logistics, scheduling, resource allocation, and engineering design, where indivisibility constraints — such as yes/no decisions or whole-unit quantities — arise naturally. |
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