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| Multi-Objective Mixed-Integer Programming× | Multi-Objective Optimization× | |
|---|---|---|
| Fagområde | Simulering | Simulering |
| Familie | Process / pipeline | Process / pipeline |
| Oprindelsesår≠ | 1980s–2000s | 1896 (concept); 1989–2002 (evolutionary algorithms era) |
| Ophavsperson≠ | Ehrgott, M.; Mavrotas, G. and others in multi-criteria optimization | Vilfredo Pareto (concept); modern computational formulation by Goldberg and Deb et al. |
| Type≠ | Mathematical optimization | Optimization framework |
| Oprindelig kilde≠ | Ehrgott, M. (2005). Multicriteria Optimization (2nd ed.). Springer, Berlin. ISBN: 9783540213987 | Deb, K. (2001). Multi-Objective Optimization Using Evolutionary Algorithms. Wiley, Chichester. ISBN: 9780471873396 |
| Aliasser | MO-MIP, Multi-criteria MIP, MOMIP, Multi-objective MILP | MOO, Multi-Criteria Optimization, Vector Optimization, Pareto Optimization |
| Relaterede≠ | 5 | 3 |
| Resumé≠ | 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. | Multi-Objective Optimization (MOO) is a mathematical and computational framework for finding solutions that simultaneously optimize two or more conflicting objective functions. Rather than collapsing all goals into a single scalar, MOO produces a set of trade-off solutions — the Pareto front — from which a decision-maker selects according to preference. It is widely used in engineering design, operations research, logistics, economics, and policy analysis. |
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