Confronta i metodi

	Analisi di Sensitività Multi-Obiettivo	Ottimizzazione Multi-Obiettivo
Campo	Simulazione	Simulazione
Famiglia	Process / pipeline	Process / pipeline
Anno di origine	1990s–2000s	1896 (concept); 1989–2002 (evolutionary algorithms era)
Ideatore	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.
Tipo	Analytical technique — parametric sensitivity across multiple objectives	Optimization framework
Fonte seminale	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
Alias	MOSA, Multi-criteria sensitivity analysis, Pareto sensitivity analysis, Multi-objective SA	MOO, Multi-Criteria Optimization, Vector Optimization, Pareto Optimization
Correlati	4	3
Sintesi	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.