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Robusno višekriterijsko optimiranje×Analiza osjetljivosti×
PodručjeSimulacijaDonošenje odluka
ObiteljProcess / pipelineMCDM
Godina nastanka20062004
TvoracDeb, K. & Gupta, H.Saltelli, A., Tarantola, S., Campolongo, F., Ratto, M.
VrstaOptimization frameworkRobustness wrapper — parameter / weight perturbation sensitivity indices
Temeljni izvorDeb, K., & Gupta, H. (2006). Introducing robustness in multi-objective optimization. Evolutionary Computation, 14(4), 463–494. DOI ↗Saltelli, A., Tarantola, S., Campolongo, F., Ratto, M. (2004). Sensitivity Analysis in Practice. Wiley, Chichester DOI ↗
Drugi naziviRMOO, Robust MOO, Robust Pareto Optimization, Uncertainty-Robust Multi-Objective Optimization
Srodne40
SažetakRobust 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.
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ScholarGateUsporedite metode: Robust Multi-Objective Optimization · SENSITIVITY-ANALYSIS. Preuzeto 2026-06-15 s https://scholargate.app/hr/compare