Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Робастное целочисленное программирование× | Смешанное целочисленное программирование× | |
|---|---|---|
| Область | Имитационное моделирование | Имитационное моделирование |
| Семейство | Process / pipeline | Process / pipeline |
| Год появления≠ | 2003 | 1958–1960 |
| Автор метода≠ | Bertsimas, D. and Sim, M. | Ralph Gomory (branch-and-bound cuts, 1958); Land & Doig (branch-and-bound, 1960) |
| Тип≠ | Deterministic robust optimization with integer variables | Mathematical optimization |
| Основополагающий источник≠ | Bertsimas, D., Sim, M. (2003). Robust discrete optimization and network flows. Mathematical Programming, 98(1-3), 49-71. DOI ↗ | Nemhauser, G. L., Wolsey, L. A. (1988). Integer and Combinatorial Optimization. Wiley-Interscience, New York. ISBN: 9780471359432 |
| Другие названия | RIP, Robust IP, Robust Combinatorial Optimization, Integer Robust Optimization | MIP, Mixed-Integer Linear Programming, MILP, Integer Programming |
| Связанные | 6 | 6 |
| Сводка≠ | Robust Integer Programming (RIP) finds integer or binary solutions that remain feasible and near-optimal across all scenarios in a prescribed uncertainty set. Rather than assuming exact knowledge of data, RIP hedges against the worst-case realization of uncertain costs or constraint coefficients, delivering decisions that are guaranteed to perform well even when inputs deviate from their nominal values. | 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. |
| ScholarGateНабор данных ↗ |
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