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| Simulazione di code per scenari di policy× | Modello di Markov× | |
|---|---|---|
| Campo | Simulazione | Simulazione |
| Famiglia | Process / pipeline | Process / pipeline |
| Anno di origine≠ | 1909 (queueing theory); scenario application from 1960s–1970s OR literature | 1906 |
| Ideatore≠ | Erlang, A. K. (foundation); generalized by operations research community | Andrei Markov |
| Tipo≠ | Comparative simulation experiment | Probabilistic state-transition model |
| Fonte seminale≠ | 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 |
| Alias | PSQS, policy queueing analysis, queueing policy comparison, scenario-based queueing model | Markov Chain, Discrete-Time Markov Chain, DTMC, Markov Process |
| Correlati | 5 | 5 |
| Sintesi≠ | 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. |
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