论文标题
排队系统中到达的聚类：自回归有条件持续时间方法
Clustering of Arrivals in Queueing Systems: Autoregressive Conditional Duration Approach
论文作者
论文摘要
通常假定排队系统中的到达是独立的，并且是指数分布的。但是，我们对在线书店的分析表明，存在一个自相关结构。首先，我们调整了昼夜和季节性模式的期限间。其次，我们基于自动回归条件持续时间（ACD）模型的精神的广义伽马分布，通过广义自回旋评分（气体）模型对跨性别时间进行了调整。第三，在一项仿真研究中，我们研究了动态到达模型对带有单个和多个服务器排队系统中客户数量，繁忙时期以及响应时间的影响。我们发现，忽略自相关结构会大大低估绩效指标，因此次优决策。所提出的方法是在实践中治疗到达聚类的一般方法。
Arrivals in queueing systems are typically assumed to be independent and exponentially distributed. Our analysis of an online bookshop, however, shows that there is an autocorrelation structure present. First, we adjust the inter-arrival times for diurnal and seasonal patterns. Second, we model adjusted inter-arrival times by the generalized autoregressive score (GAS) model based on the generalized gamma distribution in the spirit of the autoregressive conditional duration (ACD) models. Third, in a simulation study, we investigate the effects of the dynamic arrival model on the number of customers, the busy period, and the response time in queueing systems with single and multiple servers. We find that ignoring the autocorrelation structure leads to significantly underestimated performance measures and consequently suboptimal decisions. The proposed approach serves as a general methodology for the treatment of arrivals clustering in practice.
