{"title":"A hybrid genetic algorithm for scheduling jobs sharing multiple resources under uncertainty","authors":"Hanyu Gu, Hue Chi Lam, Yakov Zinder","doi":"10.1016/j.ejco.2022.100050","DOIUrl":null,"url":null,"abstract":"<div><p>This study addresses the scheduling problem where every job requires several types of resources. At every point in time, the capacity of resources is limited. When necessary, the capacity can be increased at a cost. Each job has a due date, and the processing times of jobs are random variables with a known probability distribution. The considered problem is to determine a schedule that minimises the total cost, which consists of the cost incurred due to the violation of resource limits and the total tardiness of jobs. A genetic algorithm enhanced by local search is proposed. The sample average approximation method is used to construct a confidence interval for the optimality gap of the obtained solutions. Computational study on the application of the sample average approximation method and genetic algorithm is presented. It is revealed that the proposed method is capable of providing high-quality solutions to large instances in a reasonable time.</p></div>","PeriodicalId":51880,"journal":{"name":"EURO Journal on Computational Optimization","volume":"10 ","pages":"Article 100050"},"PeriodicalIF":2.6000,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2192440622000260/pdfft?md5=ee7f15de9e360359d6fb7832f6237849&pid=1-s2.0-S2192440622000260-main.pdf","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"EURO Journal on Computational Optimization","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2192440622000260","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"OPERATIONS RESEARCH & MANAGEMENT SCIENCE","Score":null,"Total":0}
引用次数: 0
Abstract
This study addresses the scheduling problem where every job requires several types of resources. At every point in time, the capacity of resources is limited. When necessary, the capacity can be increased at a cost. Each job has a due date, and the processing times of jobs are random variables with a known probability distribution. The considered problem is to determine a schedule that minimises the total cost, which consists of the cost incurred due to the violation of resource limits and the total tardiness of jobs. A genetic algorithm enhanced by local search is proposed. The sample average approximation method is used to construct a confidence interval for the optimality gap of the obtained solutions. Computational study on the application of the sample average approximation method and genetic algorithm is presented. It is revealed that the proposed method is capable of providing high-quality solutions to large instances in a reasonable time.
期刊介绍：
The aim of this journal is to contribute to the many areas in which Operations Research and Computer Science are tightly connected with each other. More precisely, the common element in all contributions to this journal is the use of computers for the solution of optimization problems. Both methodological contributions and innovative applications are considered, but validation through convincing computational experiments is desirable. The journal publishes three types of articles (i) research articles, (ii) tutorials, and (iii) surveys. A research article presents original methodological contributions. A tutorial provides an introduction to an advanced topic designed to ease the use of the relevant methodology. A survey provides a wide overview of a given subject by summarizing and organizing research results.
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