Methoden vergelijken
Bekijk de geselecteerde methoden naast elkaar; rijen die verschillen zijn gemarkeerd.
| Beleidsscenario Cellulaire Automaten× | Discrete-Event Simulation (DES)× | |
|---|---|---|
| Vakgebied | Simulatie | Simulatie |
| Familie | Process / pipeline | Process / pipeline |
| Jaar van ontstaan≠ | 1979–1997 | 1960s (formalized); modern computational form from 1970s onward |
| Grondlegger≠ | Tobler, W. (CA foundations); Clarke, K.C. et al. (policy/urban CA scenarios) | Banks, Carson, Nelson & Nicol (textbook lineage); foundational work by Tocher & Conway (1960s) |
| Type≠ | Grid-based scenario simulation | Stochastic process simulation |
| Oorspronkelijke bron≠ | Clarke, K. C., Hoppen, S., & Gaydos, L. (1997). A self-modifying cellular automaton model of historical urbanization in the San Francisco Bay area. Environment and Planning B: Planning and Design, 24(2), 247–261. DOI ↗ | Banks, J., Carson, J.S., Nelson, B.L. & Nicol, D.M. (2010). Discrete-Event System Simulation (5th ed.). Pearson. ISBN: 978-0136062127 |
| Aliassen≠ | PSCA, CA Policy Scenario Modeling, Policy-driven CA Simulation, Scenario-based Cellular Automata | DES, event-driven simulation, Ayrık Olay Simülasyonu (DES) |
| Verwant≠ | 5 | 4 |
| Samenvatting≠ | Policy Scenario Cellular Automata (PSCA) combines cellular automata simulation with structured scenario analysis to evaluate how alternative policy decisions reshape spatially distributed systems over time. Each scenario encodes a different set of transition rules or constraints, and the model iterates to reveal divergent spatial outcomes — enabling direct, visual comparison of policy consequences at the local and system level. | 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. |
| ScholarGateGegevensset ↗ |
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