{"title":"Online order acceptance and scheduling in a single machine environment","authors":"Chunyan Zheng, Jin Yu, Guohua Wan","doi":"10.1016/j.cor.2025.107028","DOIUrl":null,"url":null,"abstract":"<div><div>We consider the online order acceptance and scheduling (OAS) problem, a widely studied problem in its offline counterpart, where orders arrive online sequentially with associated rewards, arrival times, and due dates in a finite planning horizon. The objective is to make real-time order acceptance and scheduling decisions so as to maximize the total profit. To tackle this problem, we derive an upper bound on the competitive ratio of any online algorithm for the online OAS problem and introduce three algorithms (online greedy, online learning, and delay). For the online greedy algorithm, we provide a performance guarantee under the mild conditions via theoretical analysis. Furthermore, through computational studies we highlight that both the urgency of due dates of the orders and the workload level of the system can significantly influence the performance of the online algorithms. Since each proposed algorithm has its advantages and disadvantages, we categorize different scenarios for using the suitable algorithm, aiming at offering managerial insights for firms to make informed decisions.</div></div>","PeriodicalId":10542,"journal":{"name":"Computers & Operations Research","volume":"179 ","pages":"Article 107028"},"PeriodicalIF":4.1000,"publicationDate":"2025-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Computers & Operations Research","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0305054825000565","RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS","Score":null,"Total":0}
引用次数: 0
Abstract
We consider the online order acceptance and scheduling (OAS) problem, a widely studied problem in its offline counterpart, where orders arrive online sequentially with associated rewards, arrival times, and due dates in a finite planning horizon. The objective is to make real-time order acceptance and scheduling decisions so as to maximize the total profit. To tackle this problem, we derive an upper bound on the competitive ratio of any online algorithm for the online OAS problem and introduce three algorithms (online greedy, online learning, and delay). For the online greedy algorithm, we provide a performance guarantee under the mild conditions via theoretical analysis. Furthermore, through computational studies we highlight that both the urgency of due dates of the orders and the workload level of the system can significantly influence the performance of the online algorithms. Since each proposed algorithm has its advantages and disadvantages, we categorize different scenarios for using the suitable algorithm, aiming at offering managerial insights for firms to make informed decisions.
期刊介绍:
Operations research and computers meet in a large number of scientific fields, many of which are of vital current concern to our troubled society. These include, among others, ecology, transportation, safety, reliability, urban planning, economics, inventory control, investment strategy and logistics (including reverse logistics). Computers & Operations Research provides an international forum for the application of computers and operations research techniques to problems in these and related fields.