{"title":"Finding a good normal population","authors":"Sheldon M. Ross, Tianchi Zhao","doi":"10.1016/j.orl.2025.107281","DOIUrl":null,"url":null,"abstract":"<div><div>We study the problem of finding a normal population whose mean is at least as large as some specified value <em>m</em>. Assuming a sampling cost, the objective is to minimize the expected total discounted cost until there is a population whose mean is at least <em>m</em> with probability at least <em>α</em>. We propose several heuristic policies as well as a linear programming approach.</div></div>","PeriodicalId":54682,"journal":{"name":"Operations Research Letters","volume":"61 ","pages":"Article 107281"},"PeriodicalIF":0.9000,"publicationDate":"2025-03-31","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/S0167637725000422","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}
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Abstract
We study the problem of finding a normal population whose mean is at least as large as some specified value m. Assuming a sampling cost, the objective is to minimize the expected total discounted cost until there is a population whose mean is at least m with probability at least α. We propose several heuristic policies as well as a linear programming approach.
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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