Methoden vergleichen

	Robuste Optimierung – Worst-Case Mathematische Programmierung	Lineare Programmierung
Fachgebiet	Optimierung	Optimierung
Familie	Process / pipeline	Process / pipeline
Entstehungsjahr	1970s theoretical roots; modern tractable form from late 1990s–2004	1947
Urheber	Ben-Tal, El Ghaoui & Nemirovski (seminal book, 2009); Bertsimas & Sim (tractable polyhedral formulation, 2004)	George B. Dantzig
Typ	Mathematical programming framework	Mathematical programming / continuous optimization
Wegweisende Quelle	Ben-Tal, A., El Ghaoui, L. & Nemirovski, A. (2009). Robust Optimization. Princeton University Press. ISBN: 9780691143682	Dantzig, G.B. (1963). Linear Programming and Extensions. Princeton University Press. ISBN: 9780691059136
Aliasnamen	minimax optimization, worst-case optimization, Gürbüz Optimizasyon (Robust Optimization)	LP, linear optimization, Doğrusal Programlama (LP)
Verwandt	5	4
Zusammenfassung	Robust optimization is a mathematical programming framework, formalised by Ben-Tal and Nemirovski in the late 1990s and made broadly tractable by Bertsimas and Sim (2004), that finds decisions guaranteed to perform acceptably under every scenario within a predefined uncertainty set — rather than assuming parameter values are known exactly. Instead of optimising for a single expected outcome, it minimises the worst-case objective across all plausible realisations of uncertain data.	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.