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| Stochastische ganzzahlige Programmierung× | Mixed-Integer Programming× | |
|---|---|---|
| Fachgebiet | Simulation | Simulation |
| Familie | Process / pipeline | Process / pipeline |
| Entstehungsjahr≠ | 1955 | 1958–1960 |
| Urheber≠ | Dantzig, G. B.; Beale, E. M. L. | Ralph Gomory (branch-and-bound cuts, 1958); Land & Doig (branch-and-bound, 1960) |
| Typ≠ | Optimization under uncertainty with discrete decisions | Mathematical optimization |
| Wegweisende Quelle≠ | Birge, J. R., & Louveaux, F. (1997). Introduction to Stochastic Programming. Springer, New York. ISBN: 978-1-4614-0237-4 | Nemhauser, G. L., Wolsey, L. A. (1988). Integer and Combinatorial Optimization. Wiley-Interscience, New York. ISBN: 9780471359432 |
| Aliasnamen | SIP, Stochastic IP, Integer Stochastic Programming, Mixed-Integer Stochastic Programming | MIP, Mixed-Integer Linear Programming, MILP, Integer Programming |
| Verwandt | 6 | 6 |
| Zusammenfassung≠ | Stochastic Integer Programming (SIP) is an optimization framework that combines integer (discrete) decision variables with explicit probabilistic modeling of uncertainty. It seeks the best here-and-now decision that minimizes expected cost (or maximizes expected benefit) across a distribution of future scenarios, accounting for the fact that some decisions must be made before uncertainty is resolved. | 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. |
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