Methoden vergleichen

	Lineare Programmierung	Mehrzieloptimierung – Gleichzeitige Optimierung widerstreitender Ziele
Fachgebiet	Optimierung	Simulation
Familie	Process / pipeline	Process / pipeline
Entstehungsjahr	1947	1896 (concept); 1989–2002 (evolutionary algorithms era)
Urheber	George B. Dantzig	Vilfredo Pareto (concept); modern computational formulation by Goldberg and Deb et al.
Typ	Mathematical programming / continuous optimization	Optimization framework
Wegweisende Quelle	Dantzig, G.B. (1963). Linear Programming and Extensions. Princeton University Press. ISBN: 9780691059136	Deb, K. (2001). Multi-Objective Optimization Using Evolutionary Algorithms. Wiley, Chichester. ISBN: 9780471873396
Aliasnamen	LP, linear optimization, Doğrusal Programlama (LP)	MOO, Multi-Criteria Optimization, Vector Optimization, Pareto Optimization
Verwandt	4	3
Zusammenfassung	Linear programming (LP), pioneered by George B. Dantzig in 1947, is a mathematical method for finding the best value of a linear objective function — such as minimum cost or maximum profit — subject to a set of linear inequality and equality constraints. It is the foundational technique in operations research and underlies production planning, resource allocation, logistics, diet problems, and countless other decision-making scenarios across engineering, economics, and the natural sciences.	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.