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| Lineare Programmierung mit deterministischen Parametern× | Mixed-Integer Programming× | |
|---|---|---|
| Fachgebiet | Simulation | Simulation |
| Familie | Process / pipeline | Process / pipeline |
| Entstehungsjahr≠ | 1947 | 1958–1960 |
| Urheber≠ | George B. Dantzig | Ralph Gomory (branch-and-bound cuts, 1958); Land & Doig (branch-and-bound, 1960) |
| Typ≠ | Deterministic mathematical optimization | Mathematical optimization |
| Wegweisende Quelle≠ | Dantzig, G. B. (1963). Linear Programming and Extensions. Princeton University Press, Princeton, NJ. ISBN: 9780691059136 | Nemhauser, G. L., Wolsey, L. A. (1988). Integer and Combinatorial Optimization. Wiley-Interscience, New York. ISBN: 9780471359432 |
| Aliasnamen | Classical LP, Deterministic LP, DLP, Linear Optimization | MIP, Mixed-Integer Linear Programming, MILP, Integer Programming |
| Verwandt≠ | 5 | 6 |
| Zusammenfassung≠ | Deterministic Linear Programming (DLP) is the classical form of linear programming in which all objective function coefficients, constraint coefficients, and right-hand-side values are known with certainty. It finds the optimal allocation of resources to maximize or minimize a linear objective subject to linear constraints, providing an exact, reproducible solution under fixed, certain data. | 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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