Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Смешанное целочисленное программирование с множеством целевых функций× | Смешанное целочисленное программирование× | |
|---|---|---|
| Область | Имитационное моделирование | Имитационное моделирование |
| Семейство | Process / pipeline | Process / pipeline |
| Год появления≠ | 1980s–2000s | 1958–1960 |
| Автор метода≠ | Ehrgott, M.; Mavrotas, G. and others in multi-criteria optimization | Ralph Gomory (branch-and-bound cuts, 1958); Land & Doig (branch-and-bound, 1960) |
| Тип | Mathematical optimization | Mathematical optimization |
| Основополагающий источник≠ | Ehrgott, M. (2005). Multicriteria Optimization (2nd ed.). Springer, Berlin. ISBN: 9783540213987 | Nemhauser, G. L., Wolsey, L. A. (1988). Integer and Combinatorial Optimization. Wiley-Interscience, New York. ISBN: 9780471359432 |
| Другие названия | MO-MIP, Multi-criteria MIP, MOMIP, Multi-objective MILP | MIP, Mixed-Integer Linear Programming, MILP, Integer Programming |
| Связанные≠ | 5 | 6 |
| Сводка≠ | 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. | 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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