Порівняння методів
Переглядайте обрані методи поруч; рядки з відмінностями підсвічено.
| Симуляція черг для сценаріїв політик× | Марковська модель× | |
|---|---|---|
| Галузь | Імітаційне моделювання | Імітаційне моделювання |
| Родина | Process / pipeline | Process / pipeline |
| Рік появи≠ | 1909 (queueing theory); scenario application from 1960s–1970s OR literature | 1906 |
| Автор методу≠ | Erlang, A. K. (foundation); generalized by operations research community | Andrei Markov |
| Тип≠ | Comparative simulation experiment | Probabilistic state-transition model |
| Основоположне джерело≠ | Kleinrock, L. (1975). Queueing Systems, Volume 1: Theory. Wiley-Interscience, New York. ISBN: 978-0471491101 | Norris, J. R. (1997). Markov Chains. Cambridge University Press, Cambridge. ISBN: 9780521633963 |
| Інші назви | PSQS, policy queueing analysis, queueing policy comparison, scenario-based queueing model | Markov Chain, Discrete-Time Markov Chain, DTMC, Markov Process |
| Пов'язані | 5 | 5 |
| Підсумок≠ | Policy Scenario Queueing Simulation applies queueing theory and discrete-event simulation to evaluate two or more competing service or resource-allocation policies under realistic demand and capacity conditions. By holding the system structure constant and varying only the policy rules, analysts can directly compare throughput, waiting times, utilization, and equity outcomes before committing to real-world implementation. | A Markov Model represents a system as a finite set of states and specifies the probability of moving from one state to another at each time step. By capturing only the current state — not the full history — it enables tractable analysis of complex dynamic processes across health economics, engineering reliability, operations research, and social-science modeling. |
| ScholarGateНабір даних ↗ |
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