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Symulacja kolejkowania oparta na agentach×Symulacja Monte Carlo×
DziedzinaSymulacjaPodejmowanie decyzji
RodzinaProcess / pipelineMCDM
Rok powstania2000s1949
TwórcaMacal, C. M. & North, M. J. (hybrid formalization); queueing theory rooted in Erlang (1909)Metropolis, N., Ulam, S.
TypHybrid simulation — agent-based + queueingRobustness wrapper — Monte Carlo uncertainty propagation
Źródło pierwotneMacal, 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 ↗
Inne nazwyAB-QS, Agent-Based Queue Simulation, ABM Queueing, Agent Queue Simulation
Pokrewne50
PodsumowanieAgent-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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ScholarGatePorównaj metody: Agent-based queueing simulation · MONTE-CARLO-SIMULATION. Pobrano 2026-06-15 z https://scholargate.app/pl/compare