论文标题
分配可观察到的战略队列
Distributionally Robust Observable Strategic Queues
论文作者
论文摘要
本文介绍了NAOR对可观察的M/M/1队列中联接或蝙蝠问题的分析。尽管马尔可夫的所有其他假设仍然存在，但我们探索了这个问题，假设在分配稳健的设置下的到达率不确定。我们首先研究了经典时刻歧义集的问题，在该集合集合中，已知的支持，均值和均值偏差偏差是已知的。接下来，我们将模型扩展到数据驱动的设置，在此设置中，决策者只能访问有限的样本。我们从个人客户，社会优化器和收入最大化器的角度开发了三个最佳加入阈值策略，以便最大化其各自最差的预期福利率。最后，我们将发现与NAOR的原始结果和传统样本平均近似方案进行了比较。
This paper presents an extension of Naor's analysis on the join-or-balk problem in observable M/M/1 queues. While all other Markovian assumptions still hold, we explore this problem assuming uncertain arrival rates under the distributionally robust settings. We first study the problem with the classical moment ambiguity set, where the support, mean, and mean-absolute deviation of the underlying distribution are known. Next, we extend the model to the data-driven setting, where decision makers only have access to a finite set of samples. We develop three optimal joining threshold strategies from the perspective of an individual customer, a social optimizer, and a revenue maximizer, such that their respective worst-case expected benefit rates are maximized. Finally, we compare our findings with Naor's original results and the traditional sample average approximation scheme.
