Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Программирование целочисленных сценариев политики× | Робастное целочисленное программирование× | |
|---|---|---|
| Область | Имитационное моделирование | Имитационное моделирование |
| Семейство | Process / pipeline | Process / pipeline |
| Год появления≠ | 1950s–1960s (scenario extension: 1990s onwards) | 2003 |
| Автор метода≠ | Operations research community (Dantzig, Gomory, and others) | Bertsimas, D. and Sim, M. |
| Тип≠ | Discrete combinatorial optimization under scenario uncertainty | Deterministic robust optimization with integer variables |
| Основополагающий источник≠ | Birge, J. R., & Louveaux, F. (2011). Introduction to Stochastic Programming (2nd ed.). Springer. ISBN: 9781461402367 | Bertsimas, D., Sim, M. (2003). Robust discrete optimization and network flows. Mathematical Programming, 98(1-3), 49-71. DOI ↗ |
| Другие названия | PSIP, scenario-based integer programming, policy-driven IP, scenario integer optimization | RIP, Robust IP, Robust Combinatorial Optimization, Integer Robust Optimization |
| Связанные≠ | 2 | 6 |
| Сводка≠ | Policy Scenario Integer Programming (PSIP) solves an integer programming model — where some or all decision variables must take whole-number values — separately under each of several distinct policy scenarios, then compares objective values, feasibility, and solution structures to identify which policy environment leads to the best discrete allocation or assignment outcome. | 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. |
| ScholarGateНабор данных ↗ |
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