Compară metode
Examinează metodele selectate una lângă alta; rândurile care diferă sunt evidențiate.
| Programare pe Obiective Bazată pe Agenți× | Programarea Stocastică a Obiectivelor× | |
|---|---|---|
| Domeniu | Simulare | Simulare |
| Familie | Process / pipeline | Process / pipeline |
| Anul apariției≠ | 1990s-2000s (hybrid integration) | 1968 |
| Autorul original≠ | Charnes, Cooper (GP); Schelling, Holland (ABM foundations) | Contini, B. (building on Charnes & Cooper's chance-constrained programming) |
| Tip≠ | Hybrid simulation-optimization | Stochastic multi-goal optimization |
| Sursa seminală≠ | Charnes, 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 ↗ |
| Denumiri alternative | ABGP, Agent-Based GP, ABM-GP, Agent-Driven Goal Programming | SGP, Stochastic GP, Chance-Constrained Goal Programming, Probabilistic Goal Programming |
| Înrudite≠ | 5 | 6 |
| Rezumat≠ | Agent-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. |
| ScholarGateSet de date ↗ |
|
|