قارن الطرق

	قانون ليتل (L = λW)	المحاكاة بالحدث المنفصل (DES)	نموذج طابور M/M/c: نموذج طابور متعدد الخوادم
المجال	بحوث العمليات	المحاكاة	بحوث العمليات
العائلة	Regression model	Process / pipeline	Regression model
سنة النشأة	1961	1960s (formalized); modern computational form from 1970s onward	1998
صاحب الطريقة	John D. C. Little	Banks, Carson, Nelson & Nicol (textbook lineage); foundational work by Tocher & Conway (1960s)	Queueing-theory tradition; Gross & Harris
النوع	Exact queueing identity	Stochastic process simulation	Multi-server Markovian queueing model
المصدر التأسيسي	Little, J. D. C. (1961). A proof for the queuing formula: L = λW. Operations Research, 9(3), 383–387.	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
الأسماء البديلة	L = λW Theorem, Little's Theorem, Little's Result, Little Yasası	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
ذات صلة	3	4	3
الملخص	Little's Law is a fundamental theorem in queueing theory that relates the long-run average number of items in a stable system (L) to the long-run average arrival rate (λ) and the long-run average time an item spends in the system (W), expressed as L = λW. Introduced and rigorously proved by John D. C. Little in 1961, the law holds for virtually any stable stochastic system, requiring no assumptions about arrival distributions, service distributions, or queue disciplines.	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.