Sammenlign metoder

	Analyse av flere målsettinger i scenarier	Multimål-optimering
Fagfelt	Simulering	Simulering
Familie	Process / pipeline	Process / pipeline
Opprinnelsesår	2013 (integrated framework); scenario analysis roots: 1967	1896 (concept); 1989–2002 (evolutionary algorithms era)
Opphavsperson	Stewart, French & Rios (integration formalized); scenario analysis roots: Kahn & Wiener (1967)	Vilfredo Pareto (concept); modern computational formulation by Goldberg and Deb et al.
Type	Structured qualitative-quantitative hybrid	Optimization framework
Opprinnelig kilde	Stewart, T. J., French, S., & Rios, J. (2013). Integrating multicriteria decision analysis and scenario planning: Review and extension. Omega, 41(4), 679-688.	Deb, K. (2001). Multi-Objective Optimization Using Evolutionary Algorithms. Wiley, Chichester. ISBN: 9780471873396
Alias	MOSA, Multi-criteria scenario analysis, Multi-objective futures analysis, MO-scenario analysis	MOO, Multi-Criteria Optimization, Vector Optimization, Pareto Optimization
Relaterte	4	3
Sammendrag	Multi-objective Scenario Analysis (MOSA) is a structured method that constructs a set of plausible future scenarios and evaluates each scenario against multiple competing objectives or criteria. By making trade-offs explicit across objectives and across possible futures, it supports strategic decisions where uncertainty about the future and conflicts between goals co-exist. It is widely applied in energy planning, climate adaptation, public policy, and corporate strategy.	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.