Comparar métodos

	Otimização Multiobjetivo	Algoritmo Genético	Goal Programming	Programação Inteira Mista
Área	Simulação	Otimização	Tomada de decisão	Simulação
Família	Process / pipeline	Process / pipeline	MCDM	Process / pipeline
Ano de origem	1896 (concept); 1989–2002 (evolutionary algorithms era)	1975	1955	1958–1960
Autor original	Vilfredo Pareto (concept); modern computational formulation by Goldberg and Deb et al.	John Henry Holland	Charnes, A., Cooper, W. W.	Ralph Gomory (branch-and-bound cuts, 1958); Land & Doig (branch-and-bound, 1960)
Tipo	Optimization framework	Population-based metaheuristic	Multi-objective optimisation — weighted/lexicographic goal deviation minimisation	Mathematical optimization
Fonte seminal	Deb, K. (2001). Multi-Objective Optimization Using Evolutionary Algorithms. Wiley, Chichester. ISBN: 9780471873396	Holland, J.H. (1975). Adaptation in Natural and Artificial Systems. University of Michigan Press.	Charnes, A., Cooper, W. W. (1955). Optimal estimation of executive compensation by linear programming. Management Science	Nemhauser, G. L., Wolsey, L. A. (1988). Integer and Combinatorial Optimization. Wiley-Interscience, New York. ISBN: 9780471359432
Outros nomes	MOO, Multi-Criteria Optimization, Vector Optimization, Pareto Optimization	GA, evolutionary algorithm, Genetik Algoritma — Evrimsel Optimizasyon	—	MIP, Mixed-Integer Linear Programming, MILP, Integer Programming
Relacionados	3	5	8	6
Resumo	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.	A genetic algorithm (GA) is a population-based metaheuristic optimization method introduced by John Henry Holland (1975) that mimics the principles of natural selection. It maintains a population of candidate solutions and iteratively improves them through selection, crossover, and mutation operators, making it especially powerful on discontinuous, non-convex, and multi-modal search spaces where classical gradient-based methods fail.	GOAL-PROGRAMMING (Goal Programming — Minimise deviations from multiple aspiration levels) is a ranking multi-criteria decision-making (MCDM) method introduced by Charnes, A., Cooper, W. W. in 1955. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.	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.