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While non-Markov processes were introduced in the previous chapter, this chapter provides a detailed analysis of non-Markov queueing models. Both transient and steady-state distributions are derived for the various non-Markov queueing models such as M/G/1, GI/M/1, GI/G/1, M/G/1/N, GI/M/1/N, GI/G/1/N, M/G/c, GI/M/c, GI/G/c, M/G/c/N, M/G/c/c and \(GI/G/\infty \) . These queueing models and many of their variants are studied by using a variety of methodologies including embedded Markov chain, supplementary variable analysis and Markov regenerative theory.

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General Queueing Models

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  • 发表日期：

    2025年

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  • Dharmaraja Selvamuthu

摘要

While non-Markov processes were introduced in the previous chapter, this chapter provides a detailed analysis of non-Markov queueing models. Both transient and steady-state distributions are derived for the various non-Markov queueing models such as M/G/1, GI/M/1, GI/G/1, M/G/1/N, GI/M/1/N, GI/G/1/N, M/G/c, GI/M/c, GI/G/c, M/G/c/N, M/G/c/c and \(GI/G/\infty \) . These queueing models and many of their variants are studied by using a variety of methodologies including embedded Markov chain, supplementary variable analysis and Markov regenerative theory.