{"title":"Optimal control of queues with demand-driven discharge","authors":"Guergana P. Ilieva, Hayriye Ayhan","doi":"10.1016/j.orl.2024.107220","DOIUrl":null,"url":null,"abstract":"<div><div>We consider a Markovian queueing system with finite buffer space. Arriving customers belong to different classes and have class dependent service rates. At the time of an arrival, if the system is full, one of the existing customers has to be discharged prematurely, incurring a class dependent cost, whereas class dependent rewards are earned upon successful service completions. Our objective is to determine which customer class to discharge prematurely in order to maximize the long-run average profit.</div></div>","PeriodicalId":54682,"journal":{"name":"Operations Research Letters","volume":"59 ","pages":"Article 107220"},"PeriodicalIF":0.8000,"publicationDate":"2024-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Operations Research Letters","FirstCategoryId":"91","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0167637724001561","RegionNum":4,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"OPERATIONS RESEARCH & MANAGEMENT SCIENCE","Score":null,"Total":0}
引用次数: 0
Abstract
We consider a Markovian queueing system with finite buffer space. Arriving customers belong to different classes and have class dependent service rates. At the time of an arrival, if the system is full, one of the existing customers has to be discharged prematurely, incurring a class dependent cost, whereas class dependent rewards are earned upon successful service completions. Our objective is to determine which customer class to discharge prematurely in order to maximize the long-run average profit.
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.
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