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  2. Queueing theory - Wikipedia

    en.wikipedia.org/wiki/Queueing_theory

    Queueing theory is the mathematical study of waiting lines, or queues. [1] A queueing model is constructed so that queue lengths and waiting time can be predicted. [1] Queueing theory is generally considered a branch of operations research because the results are often used when making business decisions about the resources needed to provide a ...

  3. M/D/1 queue - Wikipedia

    en.wikipedia.org/wiki/M/D/1_queue

    where τ is the mean service time; σ 2 is the variance of service time; and ρ=λτ < 1, λ being the arrival rate of the customers. For M/M/1 queue, the service times are exponentially distributed, then σ 2 = τ 2 and the mean waiting time in the queue denoted by W M is given by the following equation: [5]

  4. M/M/1 queue - Wikipedia

    en.wikipedia.org/wiki/M/M/1_queue

    Service times have an exponential distribution with rate parameter μ in the M/M/1 queue, where 1/μ is the mean service time. All arrival times and services times are (usually) assumed to be independent of one another. [2] A single server serves customers one at a time from the front of the queue, according to a first-come, first-served ...

  5. G/G/1 queue - Wikipedia

    en.wikipedia.org/wiki/G/G/1_queue

    Kingman's formula gives an approximation for the mean waiting time in a G/G/1 queue. [6] Lindley's integral equation is a relationship satisfied by the stationary waiting time distribution which can be solved using the Wiener–Hopf method. [7]

  6. Mean sojourn time - Wikipedia

    en.wikipedia.org/wiki/Mean_sojourn_time

    The mean sojourn time (or sometimes mean waiting time) for an object in a dynamical system is the amount of time an object is expected to spend in a system before leaving the system permanently. This concept is widely used in various fields, including physics, chemistry, and stochastic processes, to study the behavior of systems over time.

  7. M/G/1 queue - Wikipedia

    en.wikipedia.org/wiki/M/G/1_queue

    where g(s) is the Laplace transform of the service time probability density function. [8] In the case of an M/M/1 queue where service times are exponentially distributed with parameter μ, g(s) = μ/(μ + s). This can be solved for individual state probabilities either using by direct computation or using the method of supplementary variables.

  8. Residual time - Wikipedia

    en.wikipedia.org/wiki/Residual_time

    Another way to phrase residual time is "how much more time is there to wait?". The residual time is very important in most of the practical applications of renewal processes: In queueing theory, it determines the remaining time, that a newly arriving customer to a non-empty queue has to wait until being served. [1]

  9. G/M/1 queue - Wikipedia

    en.wikipedia.org/wiki/G/M/1_queue

    It is an extension of an M/M/1 queue, where this renewal process must specifically be a Poisson process (so that interarrival times have exponential distribution). Models of this type can be solved by considering one of two M/G/1 queue dual systems, one proposed by Ramaswami and one by Bright.