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Granska de valda metoderna sida vid sida; rader som skiljer sig är markerade.
| Robust diskret händelsestyrd simulering× | Diskret händelsessimulering (DES)× | |
|---|---|---|
| Ämnesområde | Simulering | Simulering |
| Familj | Process / pipeline | Process / pipeline |
| Ursprungsår≠ | 1990s–2000s | 1960s (formalized); modern computational form from 1970s onward |
| Upphovsperson≠ | Banks, Carson, Nelson, Nicol (canonical DES); robust extensions: operations research community | Banks, Carson, Nelson & Nicol (textbook lineage); foundational work by Tocher & Conway (1960s) |
| Typ≠ | Simulation with robustness analysis | Stochastic process simulation |
| Ursprungskälla≠ | Banks, J., Carson, J. S., Nelson, B. L., & Nicol, D. M. (2010). Discrete-Event System Simulation (5th ed.). Prentice Hall. ISBN: 9780136062127 | Banks, J., Carson, J.S., Nelson, B.L. & Nicol, D.M. (2010). Discrete-Event System Simulation (5th ed.). Pearson. ISBN: 978-0136062127 |
| Alias≠ | Robust DES, Uncertainty-Aware DES, Robust DEVS, Resilient Discrete-Event Simulation | DES, event-driven simulation, Ayrık Olay Simülasyonu (DES) |
| Närliggande≠ | 6 | 4 |
| Sammanfattning≠ | Robust Discrete-Event Simulation (Robust DES) is a simulation methodology that extends classical discrete-event simulation by explicitly incorporating uncertainty in model parameters — such as interarrival times, service durations, and resource capacities — and evaluating system performance across worst-case or distributional uncertainty sets rather than point estimates alone. It is widely applied in manufacturing, healthcare, logistics, and supply chain systems where parameter misspecification or real-world variability can lead to misleading simulation conclusions. | Discrete-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. |
| ScholarGateDatamängd ↗ |
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