考虑顺序相关设置时间和使用时间电价的不相关并机节能调度

IF 2.6 Q2 OPERATIONS RESEARCH & MANAGEMENT SCIENCE EURO Journal on Computational Optimization Pub Date : 2023-01-01 DOI:10.1016/j.ejco.2022.100052
Hemen Sanati, Ghasem Moslehi, Mohammad Reisi-Nafchi
{"title":"考虑顺序相关设置时间和使用时间电价的不相关并机节能调度","authors":"Hemen Sanati,&nbsp;Ghasem Moslehi,&nbsp;Mohammad Reisi-Nafchi","doi":"10.1016/j.ejco.2022.100052","DOIUrl":null,"url":null,"abstract":"<div><p>Given that about half of the produced energy in the world is consumed in industries, there has been an increasing concern about optimizing energy consumption in manufacturing sectors. As one of the most effective ways, proper production scheduling to reduce energy consumption is of crucial importance among researchers and manufacturers. This paper addresses an unrelated parallel machine energy-efficient scheduling problem with sequence-dependent setup times by considering different energy consumption tariffs. The setup times are studied in two modes: disjointed from/jointed to processing time. For each one of these problems, two mixed-integer linear programming models have been formulated. The presented models for the problem with setup time disjointed from processing time can solve up to 16 machines and 45 jobs. In contrast, this capability is changed to 20 machines and 40 jobs for processing time jointed to the setup time problem. Furthermore, a fix and relax heuristic algorithm is presented for large-size instances, which can solve instances of up to 20 machines and 100 jobs for each of the two considered problems.</p></div>","PeriodicalId":51880,"journal":{"name":"EURO Journal on Computational Optimization","volume":"11 ","pages":"Article 100052"},"PeriodicalIF":2.6000,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Unrelated parallel machine energy-efficient scheduling considering sequence-dependent setup times and time-of-use electricity tariffs\",\"authors\":\"Hemen Sanati,&nbsp;Ghasem Moslehi,&nbsp;Mohammad Reisi-Nafchi\",\"doi\":\"10.1016/j.ejco.2022.100052\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>Given that about half of the produced energy in the world is consumed in industries, there has been an increasing concern about optimizing energy consumption in manufacturing sectors. As one of the most effective ways, proper production scheduling to reduce energy consumption is of crucial importance among researchers and manufacturers. This paper addresses an unrelated parallel machine energy-efficient scheduling problem with sequence-dependent setup times by considering different energy consumption tariffs. The setup times are studied in two modes: disjointed from/jointed to processing time. For each one of these problems, two mixed-integer linear programming models have been formulated. The presented models for the problem with setup time disjointed from processing time can solve up to 16 machines and 45 jobs. In contrast, this capability is changed to 20 machines and 40 jobs for processing time jointed to the setup time problem. Furthermore, a fix and relax heuristic algorithm is presented for large-size instances, which can solve instances of up to 20 machines and 100 jobs for each of the two considered problems.</p></div>\",\"PeriodicalId\":51880,\"journal\":{\"name\":\"EURO Journal on Computational Optimization\",\"volume\":\"11 \",\"pages\":\"Article 100052\"},\"PeriodicalIF\":2.6000,\"publicationDate\":\"2023-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"EURO Journal on Computational Optimization\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S2192440622000284\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"OPERATIONS RESEARCH & MANAGEMENT SCIENCE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"EURO Journal on Computational Optimization","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2192440622000284","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: 1

摘要

鉴于世界上大约一半的生产能源是在工业中消耗的,人们越来越关注如何优化制造业的能源消耗。合理的生产调度作为降低能耗的最有效途径之一,受到了科研人员和生产企业的重视。本文通过考虑不同的能源消耗电价,研究了具有顺序依赖设置时间的不相关并行机节能调度问题。研究了两种模式下的装配时间:从/装配到加工时间。对于每一个问题,都建立了两个混合整数线性规划模型。针对设置时间与加工时间脱节的问题,所提出的模型可以求解多达16台机器和45个作业。相比之下,由于处理时间与设置时间问题相关,此功能更改为20台机器和40个作业。此外,针对大型实例,提出了一种修正和放松启发式算法,该算法可以为两个考虑的问题中的每个问题解决多达20台机器和100个作业的实例。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
Unrelated parallel machine energy-efficient scheduling considering sequence-dependent setup times and time-of-use electricity tariffs

Given that about half of the produced energy in the world is consumed in industries, there has been an increasing concern about optimizing energy consumption in manufacturing sectors. As one of the most effective ways, proper production scheduling to reduce energy consumption is of crucial importance among researchers and manufacturers. This paper addresses an unrelated parallel machine energy-efficient scheduling problem with sequence-dependent setup times by considering different energy consumption tariffs. The setup times are studied in two modes: disjointed from/jointed to processing time. For each one of these problems, two mixed-integer linear programming models have been formulated. The presented models for the problem with setup time disjointed from processing time can solve up to 16 machines and 45 jobs. In contrast, this capability is changed to 20 machines and 40 jobs for processing time jointed to the setup time problem. Furthermore, a fix and relax heuristic algorithm is presented for large-size instances, which can solve instances of up to 20 machines and 100 jobs for each of the two considered problems.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
EURO Journal on Computational Optimization
EURO Journal on Computational Optimization OPERATIONS RESEARCH & MANAGEMENT SCIENCE-
CiteScore
3.50
自引率
0.00%
发文量
28
审稿时长
60 days
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.
期刊最新文献
Unboxing Tree ensembles for interpretability: A hierarchical visualization tool and a multivariate optimal re-built tree An effective hybrid decomposition approach to solve the network-constrained stochastic unit commitment problem in large-scale power systems Advances in nonlinear optimization and equilibrium problems – Special issue editorial The Marguerite Frank Award for the best EJCO paper 2023 A variable metric proximal stochastic gradient method: An application to classification problems
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1