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| Multi-objektiv følsomhedsanalyse× | Multi-Objective Optimization× | |
|---|---|---|
| Fagområde | Simulering | Simulering |
| Familie | Process / pipeline | Process / pipeline |
| Oprindelsesår≠ | 1990s–2000s | 1896 (concept); 1989–2002 (evolutionary algorithms era) |
| Ophavsperson≠ | Evolved from classical sensitivity analysis (Saltelli et al.) combined with multi-objective optimization (Pareto, 1896) | Vilfredo Pareto (concept); modern computational formulation by Goldberg and Deb et al. |
| Type≠ | Analytical technique — parametric sensitivity across multiple objectives | Optimization framework |
| Oprindelig kilde≠ | Saltelli, A., Ratto, M., Andres, T., Campolongo, F., Cariboni, J., Gatelli, D., Saisana, M., Tarantola, S. (2008). Global Sensitivity Analysis: The Primer. Wiley, Chichester. ISBN: 9780470059975 | Deb, K. (2001). Multi-Objective Optimization Using Evolutionary Algorithms. Wiley, Chichester. ISBN: 9780471873396 |
| Aliasser | MOSA, Multi-criteria sensitivity analysis, Pareto sensitivity analysis, Multi-objective SA | MOO, Multi-Criteria Optimization, Vector Optimization, Pareto Optimization |
| Relaterede≠ | 4 | 3 |
| Resumé≠ | Multi-Objective Sensitivity Analysis (MOSA) examines how changes in model parameters, weights, or assumptions affect an entire set of competing objectives simultaneously. Rather than asking how a single output shifts, MOSA tracks changes in the Pareto front or trade-off surface, revealing which parameters most destabilize multi-objective solutions and where decision-maker choices are robust versus fragile. | 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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