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探索非马尔可夫动力学:具有循环客户和在扩展伯努利休假下的阻塞行为的单服务器重试队列的随机分析。

Exploring non-Markovian dynamics: Stochastic analysis of a single-server retrial queue with recurrent clients and balking behavior under extended Bernoulli vacations.

作者信息

J Kalaiselvi, Mc Saravanarajan

机构信息

Department of Mathematics, School of Advanced Sciences, Vellore Institute of Technology, Vellore - 632014, Tamilnadu, India.

出版信息

Heliyon. 2024 Jul 5;10(13):e33919. doi: 10.1016/j.heliyon.2024.e33919. eCollection 2024 Jul 15.

DOI:10.1016/j.heliyon.2024.e33919
PMID:39670223
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11637148/
Abstract

The present study delves into the dynamics of a specific form of queueing system described as an retrial queue. Here, the queue comprises two distinct categories of clients: transit clients and recurrent clients. Transit clients are those who appear at the queue following a Poisson process, reflecting a random arrival pattern commonly seen in queueing scenarios. On the other hand, recurrent clients are predefined entities who immediately rejoin the queue once they've been served, demonstrating repetitive behavior in their interactions with the system. Once the server completes servicing a client, it initiates a vacation period. Moreover, in this approach, an optional extended vacation is also taken into account, i.e., the server may opt to indulge in an extended vacation following the initial essential Bernoulli vacation. Also, the consumers are allowed to balk. Further, the ergodicity requirement for the system's stability and then analytical findings for the stationary distribution are obtained. Additionally, various performance metrics for the system are also established. Furthermore, a comprehensive cost function is formulated and further optimized by incorporating a particle swarm optimization (PSO) approach. The convergence analysis is conducted as well, which is supported by illustrative figures. As a result, this work provides a beneficial understanding of enhancing the efficiency of such intricate queueing systems.

摘要

本研究深入探讨了一种被描述为重试队列的特定形式排队系统的动态特性。在此,队列由两类不同的客户组成:过境客户和重复客户。过境客户是那些按照泊松过程出现在队列中的客户,这反映了排队场景中常见的随机到达模式。另一方面,重复客户是预定义的实体,他们一旦得到服务就立即重新加入队列,在与系统的交互中表现出重复行为。一旦服务器完成对一个客户的服务,它就开始一个休假周期。此外,在这种方法中,还考虑了一个可选的延长休假,即服务器在最初必要的伯努利休假之后可以选择进行延长休假。而且,消费者被允许放弃。此外,获得了系统稳定性的遍历性要求以及平稳分布的分析结果。此外,还建立了系统的各种性能指标。此外,通过纳入粒子群优化(PSO)方法制定并进一步优化了一个综合成本函数。还进行了收敛性分析,并辅以说明性图形。因此,这项工作为提高此类复杂排队系统的效率提供了有益的见解。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ba7a/11637148/0c20444228a8/gr005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ba7a/11637148/5ca719331515/gr001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ba7a/11637148/a574733d2e4d/gr002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ba7a/11637148/ac6d82a375a5/gr003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ba7a/11637148/0ea287dd271c/gr006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ba7a/11637148/0e38ed950168/gr004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ba7a/11637148/0c20444228a8/gr005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ba7a/11637148/5ca719331515/gr001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ba7a/11637148/a574733d2e4d/gr002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ba7a/11637148/ac6d82a375a5/gr003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ba7a/11637148/0ea287dd271c/gr006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ba7a/11637148/0e38ed950168/gr004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ba7a/11637148/0c20444228a8/gr005.jpg

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