Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Программирование целевых установок× | Многокритериальная оптимизация× | |
|---|---|---|
| Область≠ | Принятие решений | Имитационное моделирование |
| Семейство≠ | MCDM | Process / pipeline |
| Год появления≠ | 1955 | 1896 (concept); 1989–2002 (evolutionary algorithms era) |
| Автор метода≠ | Charnes, A., Cooper, W. W. | Vilfredo Pareto (concept); modern computational formulation by Goldberg and Deb et al. |
| Тип≠ | Multi-objective optimisation — weighted/lexicographic goal deviation minimisation | Optimization framework |
| Основополагающий источник≠ | Charnes, A., Cooper, W. W. (1955). Optimal estimation of executive compensation by linear programming. Management Science DOI ↗ | Deb, K. (2001). Multi-Objective Optimization Using Evolutionary Algorithms. Wiley, Chichester. ISBN: 9780471873396 |
| Другие названия≠ | — | MOO, Multi-Criteria Optimization, Vector Optimization, Pareto Optimization |
| Связанные≠ | 8 | 3 |
| Сводка≠ | GOAL-PROGRAMMING (Goal Programming — Minimise deviations from multiple aspiration levels) is a ranking multi-criteria decision-making (MCDM) method introduced by Charnes, A., Cooper, W. W. in 1955. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result. | Multi-Objective Optimization (MOO) is a mathematical and computational framework for finding solutions that simultaneously optimize two or more conflicting objective functions. Rather than collapsing all goals into a single scalar, MOO produces a set of trade-off solutions — the Pareto front — from which a decision-maker selects according to preference. It is widely used in engineering design, operations research, logistics, economics, and policy analysis. |
| ScholarGateНабор данных ↗ |
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