Porovnat metody
Prohlédněte si vybrané metody vedle sebe; řádky, které se liší, jsou zvýrazněny.
| Deterministické programování s celočíselnými proměnnými× | Robustní celočíselné programování se smíšenými proměnnými× | |
|---|---|---|
| Obor | Simulace | Simulace |
| Rodina | Process / pipeline | Process / pipeline |
| Rok vzniku≠ | 1958–1960 | 1998–2004 |
| Tvůrce≠ | Gomory, R. E.; Dantzig, G. B.; Land, A. H.; Doig, A. G. | Ben-Tal & Nemirovski; Bertsimas & Sim |
| Typ≠ | Mathematical programming / combinatorial optimization | Deterministic robust reformulation of MIP under uncertainty |
| Původní zdroj≠ | Nemhauser, G. L., Wolsey, L. A. (1988). Integer and Combinatorial Optimization. John Wiley & Sons, New York. ISBN: 9780471359432 | Bertsimas, D., Sim, M. (2004). The price of robustness. Operations Research, 52(1), 35–53. DOI ↗ |
| Další názvy | Deterministic MIP, Deterministic MILP/MIQP, Classical Mixed-Integer Programming, Deterministic MIP Optimization | RMIP, Robust MIP, Uncertain MIP, Robust MILP/MIQP |
| Příbuzné≠ | 6 | 4 |
| Shrnutí≠ | Deterministic Mixed-Integer Programming (MIP) is a mathematical optimization framework that finds the provably optimal solution to problems involving both continuous and integer decision variables under fully known, fixed coefficients and constraints. It is the foundational workhorse of operations research when all data are treated as certain. | 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. |
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