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Pdf417 2d Barcode barcode library with none Using Barcode Control SDK for .Related: EAN 128 Generator C# , Code 128 Creating Excel , Make QR Code Word Phase-type distributions In queueing applications it is often convenient to approximate the interarrival time and/or the service time by distributions that are built out of a nite sum or a nite mixture of exponentially distributed components, or a combination of both These distributions are called phase-type distributions For practical purposes it usually suf ces to use nite mixtures of Erlangian distributions with the same scale parameters or Coxian-2 distributions These distributions are discussed in detail in Appendix B The class of Coxian-2 istributions contains the hyperexponential distribution of order 2 as special case The hyperexponential distribution always has a coef cient of variation greater than or equal to 1 This distribution is particulary suited to model irregular interarrival (or service) times which have the feature that most outcomes tend to be small and large outcomes occur only occasionally The class of mixtures of Erlangian distributions with the same scale parameters is much more versatile than the class of Coxian-2 distributions and allows us to cover any positive value of the coef cient of variation In particular, a mixture of Ek 1 and Ek distributions with the same scale parameters is convenient to represent regular interarrival (or service) times which have a coef cient of variation smaller than or equal to 1 The theoretical basis for the use of mixtures of Erlangian distributions with the same scale parameters is provided by Theorem 551 This theorem states that each non-negative random variable can be approximated arbitrarily closely by a random sum of exponentially distributed phases with the same means This explains why nite mixtures of Erlangian distributions with the same scale parameters are widely used for queueing calculations Performance measures It is convenient to use the GI /G/c/c + N queue as a vehicle to introduce some basic notation Thus, we assume a multi-server queue with c identical servers and a waiting room of capacity N ( ) for customers awaiting to be served A customer who nds c + N other customers present upon arrival is rejected and has no further in uence on the system Otherwise, the arriving customer is admitted to the system and waits in queue until a server becomes available The customers arrive according to a renewal process In other words, the interarrival times are positive, independent random variables having a common probability distribution function A(t) The service times of the customers are independent random variables with a common probability distribution function B(x) and are also independent of the arrival process The queue discipline specifying which customer is to be served next is rst-come rst-served (FCFS) unless stated otherwise A server cannot be idle when customers are waiting in queue and a busy server works at unity rate A customer leaves the system upon service completion Let = the long-run average arrival rate of customers, E(S) = the mean service time of a customer. 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