Methoden vergleichen

	Multi-Objective System Dynamics	Mehrzieloptimierung – Gleichzeitige Optimierung widerstreitender Ziele
Fachgebiet	Simulation	Simulation
Familie	Process / pipeline	Process / pipeline
Entstehungsjahr	1961 (SD); multi-objective extensions from 1990s onward	1896 (concept); 1989–2002 (evolutionary algorithms era)
Urheber	Forrester, J. W. (System Dynamics); multi-objective extension by various authors	Vilfredo Pareto (concept); modern computational formulation by Goldberg and Deb et al.
Typ	Simulation / optimization hybrid	Optimization framework
Wegweisende Quelle	Sterman, J. D. (2000). Business Dynamics: Systems Thinking and Modeling for a Complex World. McGraw-Hill. ISBN: 978-0-07-231135-8	Deb, K. (2001). Multi-Objective Optimization Using Evolutionary Algorithms. Wiley, Chichester. ISBN: 9780471873396
Aliasnamen	MOSD, Multi-criteria SD, Multi-objective SD modeling, System dynamics with multiple objectives	MOO, Multi-Criteria Optimization, Vector Optimization, Pareto Optimization
Verwandt	4	3
Zusammenfassung	Multi-Objective System Dynamics (MOSD) couples the feedback-loop simulation power of System Dynamics with explicit multi-criteria optimization, enabling analysts to explore how a dynamic system can simultaneously satisfy competing policy goals — such as cost minimization, environmental sustainability, and social equity — over time.	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.