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Uigaji wa Msafara unaoendeshwa na Ajenti×Uiguzi wa Monte Carlo×
NyanjaUigajiUfanyaji Maamuzi
FamiliaProcess / pipelineMCDM
Mwaka wa asili2000s1949
MwanzilishiMacal, C. M. & North, M. J. (hybrid formalization); queueing theory rooted in Erlang (1909)Metropolis, N., Ulam, S.
AinaHybrid simulation — agent-based + queueingRobustness wrapper — Monte Carlo uncertainty propagation
Chanzo asiliaMacal, 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 ↗
Majina mbadalaAB-QS, Agent-Based Queue Simulation, ABM Queueing, Agent Queue Simulation
Zinazohusiana50
MuhtasariAgent-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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ScholarGateLinganisha mbinu: Agent-based queueing simulation · MONTE-CARLO-SIMULATION. Imepatikana 2026-06-15 kutoka https://scholargate.app/sw/compare