Methoden vergelijken

	Robuuste Multi-Objective Optimalisatie	Gevoeligheidsanalyse
Vakgebied	Simulatie	Besluitvorming
Familie	Process / pipeline	MCDM
Jaar van ontstaan	2006	2004
Grondlegger	Deb, K. & Gupta, H.	Saltelli, A., Tarantola, S., Campolongo, F., Ratto, M.
Type	Optimization framework	Robustness wrapper — parameter / weight perturbation sensitivity indices
Oorspronkelijke bron	Deb, K., & Gupta, H. (2006). Introducing robustness in multi-objective optimization. Evolutionary Computation, 14(4), 463–494.	Saltelli, A., Tarantola, S., Campolongo, F., Ratto, M. (2004). Sensitivity Analysis in Practice. Wiley, Chichester
Aliassen	RMOO, Robust MOO, Robust Pareto Optimization, Uncertainty-Robust Multi-Objective Optimization	—
Verwant	4	0
Samenvatting	Robust Multi-Objective Optimization (RMOO) is a framework for finding solutions that simultaneously optimize multiple conflicting objectives while remaining insensitive to perturbations in decision variables or problem parameters. Unlike classical MOO, RMOO explicitly incorporates uncertainty into the optimization loop, producing a robust Pareto front whose members perform well not only at the nominal design point but also across a neighbourhood of plausible operating conditions.	SENSITIVITY-ANALYSIS (Sensitivity Analysis — Systematic assessment of output variation w.r.t. input perturbations) is a ranking multi-criteria decision-making (MCDM) method introduced by Saltelli, A., Tarantola, S., Campolongo, F., Ratto, M. in 2004. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.