Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Смешанное целочисленное программирование× | Стохастическое смешанно-целочисленное программирование× | |
|---|---|---|
| Область | Имитационное моделирование | Имитационное моделирование |
| Семейство | Process / pipeline | Process / pipeline |
| Год появления≠ | 1958–1960 | 1990s–2000s |
| Автор метода≠ | Ralph Gomory (branch-and-bound cuts, 1958); Land & Doig (branch-and-bound, 1960) | Birge, J. R.; Louveaux, F.; Sen, S. |
| Тип≠ | Mathematical optimization | Stochastic optimization model |
| Основополагающий источник≠ | Nemhauser, G. L., Wolsey, L. A. (1988). Integer and Combinatorial Optimization. Wiley-Interscience, New York. ISBN: 9780471359432 | Birge, J. R., & Louveaux, F. (1997). Introduction to Stochastic Programming. Springer Series in Operations Research. New York: Springer. ISBN: 9780387982175 |
| Другие названия | MIP, Mixed-Integer Linear Programming, MILP, Integer Programming | SMIP, Stochastic MIP, Mixed-Integer Stochastic Programming, SMILP |
| 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. | 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. |
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