
doi: 10.2307/1426211 , 10.2307/1425836
The model considered here consists of n operating units which are subject to stochastic failure according to an exponential failure time distribution. These operating units are backed up by mn spare units. Failures can be of two types. With probability p (q) a failure is of type 1(2) and is sent to repair facility 1(2) for repair. Repair facility 1(2) operates as a -server queue with exponential repair times having parameter μ1 (μ2). The number of units waiting for or undergoing repair at each of the two facilities is a continuous parameter Markov chain with finite state space. This paper derives limit theorems for the stationary distribution of this Markov chain as n becomes large under the assumption that and mn grow linearly with n. These limit theorems give very useful approximations, in terms of the seven parameters characterizing the model, to a distribution that would be difficult to calculate in practice.
Applications of Markov chains and discrete-time Markov processes on general state spaces (social mobility, learning theory, industrial processes, etc.), Applications of renewal theory (reliability, demand theory, etc.), Branching processes (Galton-Watson, birth-and-death, etc.), Continuous-time Markov processes on general state spaces, Central limit and other weak theorems, Queueing theory (aspects of probability theory)
Applications of Markov chains and discrete-time Markov processes on general state spaces (social mobility, learning theory, industrial processes, etc.), Applications of renewal theory (reliability, demand theory, etc.), Branching processes (Galton-Watson, birth-and-death, etc.), Continuous-time Markov processes on general state spaces, Central limit and other weak theorems, Queueing theory (aspects of probability theory)
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