Methoden vergleichen

	Mehrzieloptimierung – Gleichzeitige Optimierung widerstreitender Ziele	Mixed-Integer Programming
Fachgebiet	Simulation	Simulation
Familie	Process / pipeline	Process / pipeline
Entstehungsjahr	1896 (concept); 1989–2002 (evolutionary algorithms era)	1958–1960
Urheber	Vilfredo Pareto (concept); modern computational formulation by Goldberg and Deb et al.	Ralph Gomory (branch-and-bound cuts, 1958); Land & Doig (branch-and-bound, 1960)
Typ	Optimization framework	Mathematical optimization
Wegweisende Quelle	Deb, K. (2001). Multi-Objective Optimization Using Evolutionary Algorithms. Wiley, Chichester. ISBN: 9780471873396	Nemhauser, G. L., Wolsey, L. A. (1988). Integer and Combinatorial Optimization. Wiley-Interscience, New York. ISBN: 9780471359432
Aliasnamen	MOO, Multi-Criteria Optimization, Vector Optimization, Pareto Optimization	MIP, Mixed-Integer Linear Programming, MILP, Integer Programming
Verwandt	3	6
Zusammenfassung	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.	Mixed-Integer Programming (MIP) is a mathematical optimization framework in which some decision variables must take integer values while others may be continuous. It generalizes linear programming and is widely used in operations research, logistics, scheduling, resource allocation, and engineering design, where indivisibility constraints — such as yes/no decisions or whole-unit quantities — arise naturally.