Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Aģentu bāzēta rindošanās simulācija× | Simulācija rindās× | |
|---|---|---|
| Nozare | Simulācija | Simulācija |
| Saime | Process / pipeline | Process / pipeline |
| Izcelsmes gads≠ | 2000s | 1909 |
| Autors≠ | Macal, C. M. & North, M. J. (hybrid formalization); queueing theory rooted in Erlang (1909) | Agner Krarup Erlang |
| Tips≠ | Hybrid simulation — agent-based + queueing | Stochastic simulation / analytical modeling |
| Pirmavots≠ | Macal, C. M., & North, M. J. (2010). Tutorial on agent-based modelling and simulation. Journal of Simulation, 4(3), 151–162. DOI ↗ | Kleinrock, L. (1975). Queueing Systems, Volume 1: Theory. Wiley-Interscience, New York. ISBN: 978-0471491101 |
| Citi nosaukumi | AB-QS, Agent-Based Queue Simulation, ABM Queueing, Agent Queue Simulation | Queue Simulation, Queuing Theory Simulation, Waiting-Line Simulation, DES-Queue |
| Saistītās≠ | 5 | 6 |
| Kopsavilkums≠ | 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. | Queueing Simulation combines classical queueing theory with discrete-event simulation to model systems where entities arrive, wait for service, and depart. It predicts performance metrics such as average waiting time, queue length, and server utilization, enabling capacity planning and bottleneck identification across service, manufacturing, healthcare, and network systems. |
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