Pooled testing in the presence of congestion

IF 0.8 4区管理学 Q4 OPERATIONS RESEARCH & MANAGEMENT SCIENCE

Operations Research Letters Pub Date : 2024-11-19 DOI:10.1016/j.orl.2024.107208

Saif Benjaafar , Oualid Jouini , Benjamin Legros , Bingnan Lu

引用次数: 0

Abstract

Pooled testing for infectious diseases has a rich history. However, the focus of much of the academic literature has been on maximizing testing throughput. While this approach maximizes the efficiency of the testing facility, it may not minimize the delay in obtaining test results. In this paper, we provide a procedure, based on a queueing model, for choosing sample batch sizes that minimize expected delay. We show that not doing so, and relying on throughput maximization, may lead to excessive delays.

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出现拥堵时的集合测试

传染病联合检测有着悠久的历史。然而，许多学术文献都把重点放在最大限度地提高检测吞吐量上。虽然这种方法能最大限度地提高检测设施的效率，但可能无法最大限度地减少获得检测结果的延迟。在本文中，我们提供了一个基于队列模型的程序，用于选择能使预期延迟最小化的样本批量大小。我们证明，如果不这样做，而是依赖于吞吐量最大化，可能会导致过度延迟。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Operations Research Letters 管理科学-运筹学与管理科学

CiteScore

2.10

自引率

9.10%

发文量

111

审稿时长

83 days

期刊介绍： Operations Research Letters is committed to the rapid review and fast publication of short articles on all aspects of operations research and analytics. Apart from a limitation to eight journal pages, quality, originality, relevance and clarity are the only criteria for selecting the papers to be published. ORL covers the broad field of optimization, stochastic models and game theory. Specific areas of interest include networks, routing, location, queueing, scheduling, inventory, reliability, and financial engineering. We wish to explore interfaces with other fields such as life sciences and health care, artificial intelligence and machine learning, energy distribution, and computational social sciences and humanities. Our traditional strength is in methodology, including theory, modelling, algorithms and computational studies. We also welcome novel applications and concise literature reviews.