Comparer des méthodes

	Méthode Non-linéaire du Meilleur-Pire	Programmation non linéaire
Domaine	Prise de décision	Optimisation
Famille	MCDM	Process / pipeline
Année d'origine	2016	2006
Auteur d'origine	Extended development of Rezaei's BWM framework	Jorge Nocedal & Stephen Wright
Type	Non-linear optimization for flexible weight derivation	Continuous mathematical optimization
Source fondatrice	Rezaei, J. (2015). Best-worst multi-criteria decision-making method: Some properties and a linear model. Journal of Cleaner Production, 229, 976-985.	Nocedal, J., & Wright, S. J. (2006). Numerical Optimization (2nd ed.). Springer. ISBN: 978-0-387-30303-1
Alias	Non-linear BWM, Nonlinear BWM	NLP optimization, Constrained nonlinear optimization, Smooth optimization, Doğrusal olmayan programlama
Apparentées	3	3
Résumé	Non-linear BWM is a variant of the Best Worst Method that replaces the linear programming formulation with non-linear optimization. Instead of minimizing the maximum deviation (Chebyshev distance), it minimizes the sum of squared deviations (L2 norm). This provides more flexible weight derivation and better accommodates uncertain or fuzzy preferences.	Nonlinear programming (NLP) is a branch of mathematical optimization concerned with problems in which the objective function or at least one constraint is nonlinear. Formalized comprehensively by Jorge Nocedal and Stephen Wright in their seminal 2006 text, NLP encompasses gradient-based algorithms — including sequential quadratic programming (SQP), interior-point methods, and quasi-Newton approaches — for finding locally or globally optimal solutions to continuous decision problems arising across engineering, economics, and the physical sciences.