Methoden vergleichen

	Multi-Objective Mixed-Integer Programming	Mehrzieloptimierung – Gleichzeitige Optimierung widerstreitender Ziele
Fachgebiet	Simulation	Simulation
Familie	Process / pipeline	Process / pipeline
Entstehungsjahr	1980s–2000s	1896 (concept); 1989–2002 (evolutionary algorithms era)
Urheber	Ehrgott, M.; Mavrotas, G. and others in multi-criteria optimization	Vilfredo Pareto (concept); modern computational formulation by Goldberg and Deb et al.
Typ	Mathematical optimization	Optimization framework
Wegweisende Quelle	Ehrgott, M. (2005). Multicriteria Optimization (2nd ed.). Springer, Berlin. ISBN: 9783540213987	Deb, K. (2001). Multi-Objective Optimization Using Evolutionary Algorithms. Wiley, Chichester. ISBN: 9780471873396
Aliasnamen	MO-MIP, Multi-criteria MIP, MOMIP, Multi-objective MILP	MOO, Multi-Criteria Optimization, Vector Optimization, Pareto Optimization
Verwandt	5	3
Zusammenfassung	Multi-Objective Mixed-Integer Programming (MO-MIP) is an optimization framework that simultaneously optimizes two or more conflicting objective functions subject to linear or nonlinear constraints, where some decision variables are restricted to integer values and others are continuous. It is widely applied in engineering design, supply chain planning, resource allocation, and scheduling problems that require discrete choices alongside continuous quantities.	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.