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Simulação de Eventos Discretos (DES)×Simulação de Monte Carlo×
ÁreaSimulaçãoTomada de decisão
FamíliaProcess / pipelineMCDM
Ano de origem1960s (formalized); modern computational form from 1970s onward1949
Autor originalBanks, Carson, Nelson & Nicol (textbook lineage); foundational work by Tocher & Conway (1960s)Metropolis, N., Ulam, S.
TipoStochastic process simulationRobustness wrapper — Monte Carlo uncertainty propagation
Fonte seminalBanks, J., Carson, J.S., Nelson, B.L. & Nicol, D.M. (2010). Discrete-Event System Simulation (5th ed.). Pearson. ISBN: 978-0136062127Metropolis, N., Ulam, S. (1949). The Monte Carlo method. Journal of the American Statistical Association DOI ↗
Outros nomesDES, event-driven simulation, Ayrık Olay Simülasyonu (DES)
Relacionados40
ResumoDiscrete-Event Simulation (DES) is a computational modeling paradigm in which the state of a system changes only at a countable sequence of points in time — the events. Between events nothing changes, so the simulation clock jumps directly from one event to the next. Formalized through the foundational textbooks of Banks, Carson, Nelson and Nicol and of Law in the 1960s–2000s, DES has become the standard tool for analyzing queuing systems, healthcare patient flows, manufacturing lines, and logistics networks where entities move through resources over time.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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ScholarGateComparar métodos: Discrete-Event Simulation · MONTE-CARLO-SIMULATION. Recuperado em 2026-06-17 de https://scholargate.app/pt/compare