Methoden vergelijken
Bekijk de geselecteerde methoden naast elkaar; rijen die verschillen zijn gemarkeerd.
| Agent-Based Queueing Simulation× | Monte Carlo Simulatie× | |
|---|---|---|
| Vakgebied≠ | Simulatie | Besluitvorming |
| Familie≠ | Process / pipeline | MCDM |
| Jaar van ontstaan≠ | 2000s | 1949 |
| Grondlegger≠ | Macal, C. M. & North, M. J. (hybrid formalization); queueing theory rooted in Erlang (1909) | Metropolis, N., Ulam, S. |
| Type≠ | Hybrid simulation — agent-based + queueing | Robustness wrapper — Monte Carlo uncertainty propagation |
| Oorspronkelijke bron≠ | Macal, C. M., & North, M. J. (2010). Tutorial on agent-based modelling and simulation. Journal of Simulation, 4(3), 151–162. DOI ↗ | Metropolis, N., Ulam, S. (1949). The Monte Carlo method. Journal of the American Statistical Association DOI ↗ |
| Aliassen≠ | AB-QS, Agent-Based Queue Simulation, ABM Queueing, Agent Queue Simulation | — |
| Verwant≠ | 5 | 0 |
| Samenvatting≠ | Agent-Based Queueing Simulation (AB-QS) combines agent-based modeling with queueing theory to simulate systems where autonomous, decision-making entities interact through waiting lines and service points. Each entity (patient, customer, job) is modeled as an independent agent with its own state and behavioral rules, enabling richer, more realistic dynamics than classical queueing models alone. | MONTE-CARLO-SIMULATION (Monte Carlo Simulation — Stochastic uncertainty propagation through MCDM model) is a ranking multi-criteria decision-making (MCDM) method introduced by Metropolis, N., Ulam, S. in 1949. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result. |
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