ScholarGate
Asistente

Comparar métodos

Revisa los métodos seleccionados uno junto a otro; las filas que difieren aparecen resaltadas.

Optimización Multiobjetivo×Programación por Objetivos×Programación Entera Mixta×
CampoSimulaciónToma de decisionesSimulación
FamiliaProcess / pipelineMCDMProcess / pipeline
Año de origen1896 (concept); 1989–2002 (evolutionary algorithms era)19551958–1960
Autor originalVilfredo Pareto (concept); modern computational formulation by Goldberg and Deb et al.Charnes, A., Cooper, W. W.Ralph Gomory (branch-and-bound cuts, 1958); Land & Doig (branch-and-bound, 1960)
TipoOptimization frameworkMulti-objective optimisation — weighted/lexicographic goal deviation minimisationMathematical optimization
Fuente seminalDeb, K. (2001). Multi-Objective Optimization Using Evolutionary Algorithms. Wiley, Chichester. ISBN: 9780471873396Charnes, A., Cooper, W. W. (1955). Optimal estimation of executive compensation by linear programming. Management Science DOI ↗Nemhauser, G. L., Wolsey, L. A. (1988). Integer and Combinatorial Optimization. Wiley-Interscience, New York. ISBN: 9780471359432
AliasMOO, Multi-Criteria Optimization, Vector Optimization, Pareto OptimizationMIP, Mixed-Integer Linear Programming, MILP, Integer Programming
Relacionados386
ResumenMulti-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.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.
ScholarGateConjunto de datos
  1. v1
  2. 2 Fuentes
  3. PUBLISHED
  1. v1
  2. 1 Fuentes
  3. PUBLISHED
  1. v1
  2. 2 Fuentes
  3. PUBLISHED

Ir a la búsqueda Download slides

ScholarGateComparar métodos: Multi-Objective Optimization · GOAL-PROGRAMMING · Mixed-Integer Programming. Recuperado el 2026-06-15 de https://scholargate.app/es/compare