文章基本信息

标题：Computationally Efficient Simulation of Queues: The R Package queuecomputer
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作者：Anthony Ebert ; Paul Wu ; Kerrie Mengersen 等
期刊名称：Journal of Statistical Software
印刷版ISSN：1548-7660
电子版ISSN：1548-7660
出版年度：2020
卷号：95
期号：1
页码：1-29
DOI：10.18637/jss.v095.i05
出版社：University of California, Los Angeles
摘要：Large networks of queueing systems model important real-world systems such as MapReduce clusters, web-servers, hospitals, call centers and airport passenger terminals. To model such systems accurately, we must infer queueing parameters from data. Unfortunately, for many queueing networks there is no clear way to proceed with parameter inference from data. Approximate Bayesian computation could offer a straightforward way to infer parameters for such networks if we could simulate data quickly enough. We present a computationally efficient method for simulating from a very general set of queueing networks with the R package queuecomputer. Remarkable speedups of more than 2 orders of magnitude are observed relative to the popular DES packages simmer and simpy. We replicate output from these packages to validate the package. The package is modular and integrates well with the popular R package dplyr. Complex queueing networks with tandem, parallel and fork/join topologies can easily be built with these two packages together. We show how to use this package with two examples: a call center and an airport terminal.
关键词：queues;queueing theory;discrete event simulation;operations research;approximate Bayesian computation;R.
其他关键词：queues;queueing theory;discrete event simulation;operations research;approximate Bayesian computation;R