Comparer des méthodes

	Programmation par objectifs multi-objectifs	Optimisation multi-objectif
Domaine	Simulation	Simulation
Famille	Process / pipeline	Process / pipeline
Année d'origine	1961	1896 (concept); 1989–2002 (evolutionary algorithms era)
Auteur d'origine	Charnes, A. and Cooper, W. W.	Vilfredo Pareto (concept); modern computational formulation by Goldberg and Deb et al.
Type	Mathematical programming / multi-criteria optimization	Optimization framework
Source fondatrice	Charnes, A., Cooper, W. W. (1961). Management Models and Industrial Applications of Linear Programming. Wiley, New York. ISBN: 978-0471148258	Deb, K. (2001). Multi-Objective Optimization Using Evolutionary Algorithms. Wiley, Chichester. ISBN: 9780471873396
Alias	MOGP, Multi-goal programming, Vector goal programming, Multi-criteria goal programming	MOO, Multi-Criteria Optimization, Vector Optimization, Pareto Optimization
Apparentées	4	3
Résumé	Multi-Objective Goal Programming (MOGP) is a mathematical programming technique that simultaneously pursues several aspirational targets by minimizing weighted deviations from each goal. Rooted in Charnes and Cooper's original goal programming framework (1961), MOGP extends it to handle multiple competing objectives, making it indispensable in operations research, supply chain design, resource allocation, and policy analysis where decision-makers must satisfy — or come close to — multiple conflicting requirements at once.	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.