M^X/G/1 优先级重演 G-queueUnder Working Breakdown and Working Vacation 的最大熵解

.. Nisha, Shweta Upadhyaya, Chandra Shekhar
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引用次数: 1

摘要

最大熵原理与排队系统的关系日益密切。最大熵原理(PME)提供了一个公正的框架,是研究复杂排队过程的一种有前途的方法。这一原理可用于评估各种广泛的工业问题中排队场景的最合适概率分布。本文探讨了一般服务批量到达重审 G 型队列的各个方面,包括工作假期、状态相关到达、优先用户和工作故障。这种等待队列在现实世界中的应用包括计算机系统、工业企业、分组交换网络和通信设施等。当服务器(操作员)忙于处理积极用户时,不利用户(或消极到达者)就会出现。消费者的到达模式遵循泊松分布。优先消费者和普通消费者是本研究考虑的两类消费者。优先消费者无需排队等候,并享有特殊的预防权,可以比普通消费者更早获得服务。最初,我们在这项研究工作中估算了包括轨道大小和长期概率在内的性能指标。然后使用最大熵方法对系统的精确等待时间和估计等待时间进行比较分析。此外,还开发了一个双目标优化模型,以同时减少消费者的等待时间和估计成本。利用所提供的分析策略,可以在服务标准和运营费用之间建立有效的平衡。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Maximum Entropy Solution for M^X/G/1 Priority Reiterate G-queue Under Working Breakdown and Working Vacation
The maximum entropy principle has grown progressively more pertinent to queueing systems. The principle of maximum entropy (PME) presents an impartial framework as a promising method to examine complex queuing processes. This principle can be employed to assess the most appropriate probability distributions for queueing scenarios in a variety of widespread industrial issues. The aspects of general service bulk arrival retrial G-queue including working vacation, state-dependent arrival, priority users, and working breakdown are all explored in this article. Real-world applications for this kind of waiting line include computer systems, industrial companies, packet-switching networks, and communication facilities, etc. The adverse users (or negative arrivals) can make an appearance when the server (operator) is preoccupied with a positive user. Consumer’s arrival patterns follow the Poisson distribution. Priority consumers and regular (ordinary) consumers are the two groups of consumers that are considered in this investigation. Priority consumers do not have to wait in line and are granted a special right of prevention that allows them to receive services before ordinary consumers. Initially, we have estimated performance metrics including orbit size and long-run probabilities in this research work. The maximum entropy approach is then used to give a comparative perusal between the system’s exact and estimated waiting times. Apart from that a bi-objective optimization model is developed to diminish both consumers waiting times and estimated costs simultaneously. It is manageable to establish an effective balance between the standard of service and operating expenses using the analytical strategy that has been provided.
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