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Имитационное моделирование дискретно-событийных систем×Метод Монте-Карло×
ОбластьИмитационное моделированиеПринятие решений
СемействоProcess / pipelineMCDM
Год появления1960s (formalised in literature through the 1980s–2000s)1949
Автор методаKelton, Law & Sadowski (formalised methodology); SIMSCRIPT (Markowitz et al., 1963) and GPSS (Gordon, 1961) were pioneering toolsMetropolis, N., Ulam, S.
ТипStochastic process simulationRobustness wrapper — Monte Carlo uncertainty propagation
Основополагающий источникKelton, W.D., Sadowski, R.P. & Zupick, N.B. (2014). Simulation with Arena (6th ed.). McGraw-Hill. ISBN: 978-0073401317Metropolis, N., Ulam, S. (1949). The Monte Carlo method. Journal of the American Statistical Association DOI ↗
Другие названияDES, discrete event simulation, Kesikli Sistem Simülasyonu (Arena / AnyLogic tarzı)
Связанные40
СводкаDiscrete-event system simulation (DES) is a computational modelling technique in which the state of a system changes only at discrete points in time — called events — such as a customer arriving, a machine starting, or a job completing. Formalised through foundational texts by Kelton, Sadowski, and Zupick (2014) and Law (2015), DES represents processes as networks of resources, queues, and activities, allowing analysts to test capacity and policy changes on a virtual model before touching the real system.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.
ScholarGateНабор данных
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  2. 2 Источники
  3. PUBLISHED
  1. v1
  2. 1 Источники
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ScholarGateСравнение методов: Discrete-Event System Simulation · MONTE-CARLO-SIMULATION. Получено 2026-06-17 из https://scholargate.app/ru/compare