Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Смешанное 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≠ | Ralph Gomory (branch-and-bound cuts, 1958); Land & Doig (branch-and-bound, 1960) | Ehrgott, M.; Mavrotas, G. and others in multi-criteria optimization |
| Тип | Mathematical optimization | Mathematical optimization |
| Основополагающий источник≠ | Nemhauser, G. L., Wolsey, L. A. (1988). Integer and Combinatorial Optimization. Wiley-Interscience, New York. ISBN: 9780471359432 | Ehrgott, M. (2005). Multicriteria Optimization (2nd ed.). Springer, Berlin. ISBN: 9783540213987 |
| Другие названия | MIP, Mixed-Integer Linear Programming, MILP, Integer Programming | MO-MIP, Multi-criteria MIP, MOMIP, Multi-objective MILP |
| Связанные≠ | 6 | 5 |
| Сводка≠ | 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. | 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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