Queueing: basic theory and applications
Elementary markov chains; Markov chain computations; Continuous time processes; Birth-death process in queues; Prototype steady-state models; Transient solutions; Time varying inputs; Imbedded markov chains; Bulk queues; Networks of queues; Special topics; Model selection and data analysis; Parameter estimation and hypothesis testing.
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ELEMENTARY MARKOV CHAINS
MARKOV CHAIN COMPUTATIONS
CONTINUOUS TIME PROCESSES
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aircraft analysis approximation arrival and service arrival event arrival process arrival rate arriving customer assume assumptions average number batch birth-death birth-death process calculate capacity channel chapter coefficients Consider customers arrive customers present develop differential equations differential-difference equations eigenvalues estimate example expected number exponential service exponentially distributed Find the probability finite follows function geometric transform hour input interarrival inverting Laplace transform limit Markov chain Markov process Markov property mean and variance minutes multi-channel number of arrivals number of customers obtained offered load operation parameters percent period pn(t Poisson arrival Poisson distribution Poisson process priority probability distribution problem queueing models queueing systems queueing theory random variable Runge-Kutta method servers service distribution service epoch service rate service time distribution set of differential-difference set of equations simulation single single-channel solve steady-state solution Suppose techniques time-varying tions transient solutions transition matrix truncated values vector waiting zero