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| Multi-Objective Mixed-Integer Programming× | Mixed-Integer Programming× | |
|---|---|---|
| Fachgebiet | Simulation | Simulation |
| Familie | Process / pipeline | Process / pipeline |
| Entstehungsjahr≠ | 1980s–2000s | 1958–1960 |
| Urheber≠ | Ehrgott, M.; Mavrotas, G. and others in multi-criteria optimization | Ralph Gomory (branch-and-bound cuts, 1958); Land & Doig (branch-and-bound, 1960) |
| Typ | Mathematical optimization | Mathematical optimization |
| Wegweisende Quelle≠ | 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 |
| Aliasnamen | MO-MIP, Multi-criteria MIP, MOMIP, Multi-objective MILP | MIP, Mixed-Integer Linear Programming, MILP, Integer Programming |
| Verwandt≠ | 5 | 6 |
| Zusammenfassung≠ | 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. |
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