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Programación por Metas Basada en Agentes×Programación por Metas Estocástica×
CampoSimulaciónSimulación
FamiliaProcess / pipelineProcess / pipeline
Año de origen1990s-2000s (hybrid integration)1968
Autor originalCharnes, Cooper (GP); Schelling, Holland (ABM foundations)Contini, B. (building on Charnes & Cooper's chance-constrained programming)
TipoHybrid simulation-optimizationStochastic multi-goal optimization
Fuente seminalCharnes, A., Cooper, W. W., & Ferguson, R. O. (1955). Optimal estimation of executive compensation by linear programming. Management Science, 1(2), 138-151. DOI ↗Contini, B. (1968). A stochastic approach to goal programming. Operations Research, 16(3), 576–586. DOI ↗
AliasABGP, Agent-Based GP, ABM-GP, Agent-Driven Goal ProgrammingSGP, Stochastic GP, Chance-Constrained Goal Programming, Probabilistic Goal Programming
Relacionados56
ResumenAgent-Based Goal Programming (ABGP) integrates agent-based simulation with goal programming optimization to model systems where multiple autonomous decision-makers pursue competing, prioritized goals. It enables researchers to study how decentralized, adaptive behavior at the agent level leads to system-level outcomes measured against predefined targets, capturing both emergence and multi-criteria satisfaction simultaneously.Stochastic Goal Programming (SGP) extends classical goal programming to handle uncertainty in goal targets, constraint coefficients, or right-hand-side parameters. By incorporating probabilistic constraints and stochastic objective components, it finds solutions that satisfy multiple goals at acceptable probability levels, making it suitable for decision problems where data are inherently uncertain or variable.
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  3. PUBLISHED

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ScholarGateComparar métodos: Agent-based goal programming · Stochastic Goal Programming. Recuperado el 2026-06-15 de https://scholargate.app/es/compare