เปรียบเทียบวิธี
ดูวิธีที่เลือกเทียบกันแบบเคียงข้าง แถวที่ต่างกันจะถูกเน้นไว้
| การจำลองเหตุการณ์แบบไม่ต่อเนื่อง (Discrete-Event Simulation - DES)× | M/M/c Queue: แบบจำลองแถวคอยหลายช่องบริการ× | |
|---|---|---|
| สาขาวิชา≠ | การจำลอง | การวิจัยดำเนินงาน |
| ตระกูล≠ | Process / pipeline | Regression model |
| ปีกำเนิด≠ | 1960s (formalized); modern computational form from 1970s onward | 1998 |
| ผู้ริเริ่ม≠ | Banks, Carson, Nelson & Nicol (textbook lineage); foundational work by Tocher & Conway (1960s) | Queueing-theory tradition; Gross & Harris |
| ประเภท≠ | Stochastic process simulation | Multi-server Markovian queueing model |
| แหล่งต้นตำรับ≠ | Banks, J., Carson, J.S., Nelson, B.L. & Nicol, D.M. (2010). Discrete-Event System Simulation (5th ed.). Pearson. ISBN: 978-0136062127 | Gross, D., & Harris, C. M. (1998). Fundamentals of Queueing Theory (3rd ed.). Wiley. ISBN: 978-0-471-17083-9 |
| ชื่อเรียกอื่น≠ | DES, event-driven simulation, Ayrık Olay Simülasyonu (DES) | Multi-Server Erlang Queue, c-Server Markovian Queue, Erlang-C Queue, Çok Sunuculu M/M/c Kuyruğu |
| ที่เกี่ยวข้อง≠ | 4 | 3 |
| สรุป≠ | 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. | The M/M/c queue is a multi-server stochastic model in which customers arrive according to a Poisson process at rate λ, are served by c identical servers each with exponentially distributed service times at rate μ, and wait in a single common queue when all servers are busy. Systematized within classical queueing theory and thoroughly treated by Gross and Harris (1998), it extends the simpler M/M/1 model to settings with parallel servers, making it the foundational tool for capacity planning in service systems. |
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