{"title":"Single-machine makespan scheduling with scenario-dependent release dates","authors":"Zhichao Geng, Yuan Gao","doi":"10.1016/j.orl.2025.107353","DOIUrl":null,"url":null,"abstract":"<div><div>This paper addresses the robust single-machine makespan scheduling problems, in which the processing times of jobs and a set of discrete scenarios are given, and the release dates of jobs are scenario-dependent. For three common criteria (absolute robustness, maximum regret and relative maximum regret) relevant to robust optimization approaches, we show that the corresponding problems are all polynomially solvable.</div></div>","PeriodicalId":54682,"journal":{"name":"Operations Research Letters","volume":"63 ","pages":"Article 107353"},"PeriodicalIF":0.9000,"publicationDate":"2025-08-06","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/S0167637725001142","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
This paper addresses the robust single-machine makespan scheduling problems, in which the processing times of jobs and a set of discrete scenarios are given, and the release dates of jobs are scenario-dependent. For three common criteria (absolute robustness, maximum regret and relative maximum regret) relevant to robust optimization approaches, we show that the corresponding problems are all polynomially solvable.
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.