Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Детерминированное смешанно-целочисленное программирование× | Смешанное целочисленное программирование с множеством целевых функций× | |
|---|---|---|
| Область | Имитационное моделирование | Имитационное моделирование |
| Семейство | Process / pipeline | Process / pipeline |
| Год появления≠ | 1958–1960 | 1980s–2000s |
| Автор метода≠ | Gomory, R. E.; Dantzig, G. B.; Land, A. H.; Doig, A. G. | Ehrgott, M.; Mavrotas, G. and others in multi-criteria optimization |
| Тип≠ | Mathematical programming / combinatorial optimization | Mathematical optimization |
| Основополагающий источник≠ | Nemhauser, G. L., Wolsey, L. A. (1988). Integer and Combinatorial Optimization. John Wiley & Sons, New York. ISBN: 9780471359432 | Ehrgott, M. (2005). Multicriteria Optimization (2nd ed.). Springer, Berlin. ISBN: 9783540213987 |
| Другие названия | Deterministic MIP, Deterministic MILP/MIQP, Classical Mixed-Integer Programming, Deterministic MIP Optimization | MO-MIP, Multi-criteria MIP, MOMIP, Multi-objective MILP |
| Связанные≠ | 6 | 5 |
| Сводка≠ | 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. | Multi-Objective Mixed-Integer Programming (MO-MIP) is an optimization framework that simultaneously optimizes two or more conflicting objective functions subject to linear or nonlinear constraints, where some decision variables are restricted to integer values and others are continuous. It is widely applied in engineering design, supply chain planning, resource allocation, and scheduling problems that require discrete choices alongside continuous quantities. |
| ScholarGateНабор данных ↗ |
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