Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Стохастическое смешанно-целочисленное программирование× | Стохастическая многокритериальная оптимизация× | |
|---|---|---|
| Область | Имитационное моделирование | Имитационное моделирование |
| Семейство | Process / pipeline | Process / pipeline |
| Год появления | 1990s–2000s | 1990s–2000s |
| Автор метода≠ | Birge, J. R.; Louveaux, F.; Sen, S. | Various (Fonseca, Fleming, Deb, Zitzler, and others) |
| Тип≠ | Stochastic optimization model | Stochastic metaheuristic optimization |
| Основополагающий источник≠ | Birge, J. R., & Louveaux, F. (1997). Introduction to Stochastic Programming. Springer Series in Operations Research. New York: Springer. ISBN: 9780387982175 | Deb, K. (2001). Multi-Objective Optimization Using Evolutionary Algorithms. Wiley, Chichester. ISBN: 9780471873396 |
| Другие названия | SMIP, Stochastic MIP, Mixed-Integer Stochastic Programming, SMILP | SMOO, Stochastic MOO, Multi-objective optimization under uncertainty, Robust multi-objective optimization |
| Связанные | 5 | 5 |
| Сводка≠ | 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. | Stochastic Multi-Objective Optimization (SMOO) is a class of methods that simultaneously optimizes two or more conflicting objectives when parameters, costs, or constraints are uncertain or random. Rather than a single optimal solution, it produces a Pareto front of non-dominated solutions, each representing a different balance among objectives under the modeled uncertainty. |
| ScholarGateНабор данных ↗ |
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