השוואת שיטות

	אופטימיזציה רב-מטרתית רובוסטית	ניתוח רגישות
תחום	סימולציה	קבלת החלטות
משפחה	Process / pipeline	MCDM
שנת המקור	2006	2004
הוגה השיטה	Deb, K. & Gupta, H.	Saltelli, A., Tarantola, S., Campolongo, F., Ratto, M.
סוג	Optimization framework	Robustness wrapper — parameter / weight perturbation sensitivity indices
מקור מכונן	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
כינויים	RMOO, Robust MOO, Robust Pareto Optimization, Uncertainty-Robust Multi-Objective Optimization	—
קשורות	4	0
תקציר	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.