串联队列服务中负载平衡和区分任务的高效分配

IF 4.4 3区 管理学 Q1 OPERATIONS RESEARCH & MANAGEMENT SCIENCE
Mohammad Delasay, Mustafa Akan
{"title":"串联队列服务中负载平衡和区分任务的高效分配","authors":"Mohammad Delasay, Mustafa Akan","doi":"10.1007/s10479-024-06202-2","DOIUrl":null,"url":null,"abstract":"<p>Within service systems, tasks can encompass diverse functionalities. In a two-phase queuing model featuring two customer priority classes, our study discerns two distinct task functionalities executed by the first-phase server (referred to as the <i>auxiliary</i> server). These tasks aim to facilitate priority-based service by the second-phase server (referred to as the <i>expert</i>). <i>Load-balancing</i> tasks aim to alleviate the expert’s workload, while <i>differentiation</i> tasks seek to enhance accurate customer prioritization in the second phase by reducing misclassifications. With customers queuing for both the auxiliary server and the expert in tandem, our investigation focuses on determining the optimal allocation of the auxiliary server’s time between these load-balancing and differentiation tasks. Through queuing optimization, we aim to minimize customers’ expected total delay cost. In scenarios where the auxiliary server is allowed to perform only one task type (either load-balancing or differentiation), we delineate the optimal solutions based on specific functional forms dictating the server’s efficiency in executing each task type. These solutions strike a balance between excess phase capacities and the square root of marginal cost-to-saving ratios arising from each task type. Additionally, we partially characterize the optimal solution in scenarios permitting both load-balancing and differentiation tasks. Notably, under high system loads, executing load-balancing tasks proves more efficient than differentiation tasks. However, the relationship between the optimal task durations and system load showcases a non-monotonic pattern. As AI decision support products increasingly enable expert providers to delegate “routine” tasks to mid-level providers, our study sheds light on the efficient allocation of different tasks to different provider types to minimize delay costs in service systems.</p>","PeriodicalId":8215,"journal":{"name":"Annals of Operations Research","volume":null,"pages":null},"PeriodicalIF":4.4000,"publicationDate":"2024-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Efficient allocation of load-balancing and differentiation tasks in tandem queue services\",\"authors\":\"Mohammad Delasay, Mustafa Akan\",\"doi\":\"10.1007/s10479-024-06202-2\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>Within service systems, tasks can encompass diverse functionalities. In a two-phase queuing model featuring two customer priority classes, our study discerns two distinct task functionalities executed by the first-phase server (referred to as the <i>auxiliary</i> server). These tasks aim to facilitate priority-based service by the second-phase server (referred to as the <i>expert</i>). <i>Load-balancing</i> tasks aim to alleviate the expert’s workload, while <i>differentiation</i> tasks seek to enhance accurate customer prioritization in the second phase by reducing misclassifications. With customers queuing for both the auxiliary server and the expert in tandem, our investigation focuses on determining the optimal allocation of the auxiliary server’s time between these load-balancing and differentiation tasks. Through queuing optimization, we aim to minimize customers’ expected total delay cost. In scenarios where the auxiliary server is allowed to perform only one task type (either load-balancing or differentiation), we delineate the optimal solutions based on specific functional forms dictating the server’s efficiency in executing each task type. These solutions strike a balance between excess phase capacities and the square root of marginal cost-to-saving ratios arising from each task type. Additionally, we partially characterize the optimal solution in scenarios permitting both load-balancing and differentiation tasks. Notably, under high system loads, executing load-balancing tasks proves more efficient than differentiation tasks. However, the relationship between the optimal task durations and system load showcases a non-monotonic pattern. As AI decision support products increasingly enable expert providers to delegate “routine” tasks to mid-level providers, our study sheds light on the efficient allocation of different tasks to different provider types to minimize delay costs in service systems.</p>\",\"PeriodicalId\":8215,\"journal\":{\"name\":\"Annals of Operations Research\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":4.4000,\"publicationDate\":\"2024-08-12\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Annals of Operations Research\",\"FirstCategoryId\":\"91\",\"ListUrlMain\":\"https://doi.org/10.1007/s10479-024-06202-2\",\"RegionNum\":3,\"RegionCategory\":\"管理学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"OPERATIONS RESEARCH & MANAGEMENT SCIENCE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Annals of Operations Research","FirstCategoryId":"91","ListUrlMain":"https://doi.org/10.1007/s10479-024-06202-2","RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"OPERATIONS RESEARCH & MANAGEMENT SCIENCE","Score":null,"Total":0}
引用次数: 0

摘要

在服务系统中,任务可以包含多种功能。在一个具有两个客户优先级的两阶段排队模型中,我们的研究发现了由第一阶段服务器(称为辅助服务器)执行的两种不同的任务功能。这些任务旨在促进第二阶段服务器(称为专家)提供基于优先级的服务。负载平衡任务旨在减轻专家的工作量,而区分任务则旨在通过减少错误分类来提高第二阶段客户优先级排序的准确性。在客户同时排队等候辅助服务器和专家的情况下,我们的研究重点是确定辅助服务器在负载平衡和区分任务之间的最佳时间分配。通过队列优化,我们的目标是使客户的预期总延迟成本最小化。在只允许辅助服务器执行一种任务类型(负载平衡或差异化)的情况下,我们根据服务器执行每种任务类型的效率的特定函数形式,划定了最佳解决方案。这些解决方案在过剩阶段容量和每种任务类型产生的边际成本与节省比率的平方根之间取得了平衡。此外,我们还部分描述了在允许负载平衡和差异化任务的情况下的最优解决方案。值得注意的是,在系统负荷较高的情况下,执行负载平衡任务比执行分化任务更有效。不过,最佳任务持续时间与系统负载之间的关系呈现出非单调模式。随着人工智能决策支持产品越来越多地让专家级服务提供者将 "常规 "任务委托给中级服务提供者,我们的研究揭示了如何高效地将不同任务分配给不同类型的服务提供者,以最大限度地降低服务系统的延迟成本。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Efficient allocation of load-balancing and differentiation tasks in tandem queue services

Efficient allocation of load-balancing and differentiation tasks in tandem queue services

Within service systems, tasks can encompass diverse functionalities. In a two-phase queuing model featuring two customer priority classes, our study discerns two distinct task functionalities executed by the first-phase server (referred to as the auxiliary server). These tasks aim to facilitate priority-based service by the second-phase server (referred to as the expert). Load-balancing tasks aim to alleviate the expert’s workload, while differentiation tasks seek to enhance accurate customer prioritization in the second phase by reducing misclassifications. With customers queuing for both the auxiliary server and the expert in tandem, our investigation focuses on determining the optimal allocation of the auxiliary server’s time between these load-balancing and differentiation tasks. Through queuing optimization, we aim to minimize customers’ expected total delay cost. In scenarios where the auxiliary server is allowed to perform only one task type (either load-balancing or differentiation), we delineate the optimal solutions based on specific functional forms dictating the server’s efficiency in executing each task type. These solutions strike a balance between excess phase capacities and the square root of marginal cost-to-saving ratios arising from each task type. Additionally, we partially characterize the optimal solution in scenarios permitting both load-balancing and differentiation tasks. Notably, under high system loads, executing load-balancing tasks proves more efficient than differentiation tasks. However, the relationship between the optimal task durations and system load showcases a non-monotonic pattern. As AI decision support products increasingly enable expert providers to delegate “routine” tasks to mid-level providers, our study sheds light on the efficient allocation of different tasks to different provider types to minimize delay costs in service systems.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
Annals of Operations Research
Annals of Operations Research 管理科学-运筹学与管理科学
CiteScore
7.90
自引率
16.70%
发文量
596
审稿时长
8.4 months
期刊介绍: The Annals of Operations Research publishes peer-reviewed original articles dealing with key aspects of operations research, including theory, practice, and computation. The journal publishes full-length research articles, short notes, expositions and surveys, reports on computational studies, and case studies that present new and innovative practical applications. In addition to regular issues, the journal publishes periodic special volumes that focus on defined fields of operations research, ranging from the highly theoretical to the algorithmic and the applied. These volumes have one or more Guest Editors who are responsible for collecting the papers and overseeing the refereeing process.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信