提出新方法优化排队系统入队速率，兼顾稳定与效率。
Admission Control of Quasi-Reversible Queueing Systems: Optimization and Reinforcement Learning
- 定义新类可逆性，推广平衡入队策略至更广系统
- 证明引入控制策略后仍保持系统稳定状态
- 适用于资源调度与智能控制，适合运筹优化研究者
本文提出一种通用方案，用于优化准可逆排队系统的到达率。首先，我们给出准可逆性的新定义，涵盖可逆性并强调客户类别的重要性；随后引入平衡到达控制策略，将Whittle网络中的平衡到达率概念推广至更广泛的准可逆排队系统。证明在准可逆系统中加入平衡到达控制策略后，系统仍保持准可逆性，并确定了平稳分布的形式。重新审视两类典型准可逆系统：Whittle网络与顺序无关队列。最后，聚焦准入控制问题，将前述成果应用于优化与强化学习框架中。
原文摘要 · Abstract (English)
In this paper, we introduce a versatile scheme for optimizing the arrival rates of quasi-reversible queueing systems. We first propose an alternative definition of quasi-reversibility that encompasses reversibility and highlights the importance of the definition of customer classes. Then we introduce balanced arrival control policies, which generalize the notion of balanced arrival rates introduced in the context of Whittle networks, to the much broader class of quasi-reversible queueing systems. We prove that supplementing a quasi-reversible queueing system with a balanced arrival-control policy preserves the quasi-reversibility, and we specify the form of the stationary measures. We revisit two canonical examples of quasi-reversible queueing systems, Whittle networks and order-independent queues. Lastly, we focus on the problem of admission control and leverage our results in the frameworks of optimization and reinforcement learning.
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