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| Multi-Objective Optimization× | Blandet-heltallig programmering× | |
|---|---|---|
| Fagområde | Simulering | Simulering |
| Familie | Process / pipeline | Process / pipeline |
| Oprindelsesår≠ | 1896 (concept); 1989–2002 (evolutionary algorithms era) | 1958–1960 |
| Ophavsperson≠ | Vilfredo Pareto (concept); modern computational formulation by Goldberg and Deb et al. | Ralph Gomory (branch-and-bound cuts, 1958); Land & Doig (branch-and-bound, 1960) |
| Type≠ | Optimization framework | Mathematical optimization |
| Oprindelig kilde≠ | Deb, K. (2001). Multi-Objective Optimization Using Evolutionary Algorithms. Wiley, Chichester. ISBN: 9780471873396 | Nemhauser, G. L., Wolsey, L. A. (1988). Integer and Combinatorial Optimization. Wiley-Interscience, New York. ISBN: 9780471359432 |
| Aliasser | MOO, Multi-Criteria Optimization, Vector Optimization, Pareto Optimization | MIP, Mixed-Integer Linear Programming, MILP, Integer Programming |
| Relaterede≠ | 3 | 6 |
| Resumé≠ | 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. | 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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