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Simulació Estocàstica d'Esdeveniments Discrets×Simulació Monte Carlo×
CampSimulacióPresa de decisions
FamíliaProcess / pipelineMCDM
Any d'origen1960s–1970s1949
Autor originalBanks, Carson, Nelson, Nicol; Law, A. M.Metropolis, N., Ulam, S.
TipusStochastic simulation modelRobustness wrapper — Monte Carlo uncertainty propagation
Font seminalBanks, J., Carson, J. S., Nelson, B. L., & Nicol, D. M. (2010). Discrete-Event System Simulation (5th ed.). Prentice Hall. ISBN: 9780136062127Metropolis, N., Ulam, S. (1949). The Monte Carlo method. Journal of the American Statistical Association DOI ↗
ÀliesStochastic DES, SDES, Probabilistic DES, Monte Carlo DES
Relacionats60
ResumStochastic Discrete-Event Simulation (Stochastic DES) models complex systems by advancing simulated time from one discrete event to the next, drawing event durations and inter-arrival times from fitted probability distributions. It is the standard technique for analyzing queues, manufacturing lines, healthcare pathways, and logistics networks under uncertainty, producing output statistics with confidence intervals.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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ScholarGateCompara mètodes: Stochastic Discrete-Event Simulation · MONTE-CARLO-SIMULATION. Recuperat el 2026-06-18 de https://scholargate.app/ca/compare