Bandingkan metode

	Optimasi Multi-Objektif Robust	Analisis Sensitivitas
Bidang	Simulasi	Pengambilan Keputusan
Keluarga	Process / pipeline	MCDM
Tahun asal	2006	2004
Pencetus	Deb, K. & Gupta, H.	Saltelli, A., Tarantola, S., Campolongo, F., Ratto, M.
Tipe	Optimization framework	Robustness wrapper — parameter / weight perturbation sensitivity indices
Sumber perintis	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
Alias	RMOO, Robust MOO, Robust Pareto Optimization, Uncertainty-Robust Multi-Objective Optimization	—
Terkait	4	0
Ringkasan	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.