{"title":"基于激励的方案对可变机器速度的车间作业调度的影响","authors":"Marc Füchtenhans, Christoph H. Glock","doi":"10.1080/00207543.2023.2266765","DOIUrl":null,"url":null,"abstract":"AbstractGiven the high demand for energy in the manufacturing industry and the increasing use of renewable but volatile energy sources, it becomes increasingly important to coordinate production and energy availability. With the help of incentive-based programmes, grid operators can incentivise consumers to adjust power demand in critical situations such that grid stability is not threatened. On the consumer side, energy-efficient scheduling models can be used to make energy consumption more flexible. This paper proposes a bi-objective job-shop scheduling problem with variable machine speeds that aims on minimising the total energy consumption and total weighted tardiness simultaneously. We use a genetic algorithm to solve the model and derive Pareto frontiers to analyse the trade-off between both conflicting objectives. We gain insights into how incentive-based programmes can be integrated into machine scheduling models and analyse the potential interdependencies and benefits that result from this integration.KEYWORDS: Job-shop schedulingenergy-efficient production planninggenetic algorithmsustainable manufacturingdemand response programmesincentive-based programmes AcknowledgementsThis paper is a revised and extended version of the conference paper ‘Energy-efficient job shop scheduling considering processing speed and incentive-based programmes’ that was presented at 10th IFAC Conference on Manufacturing Modelling, Management and Control in Nantes, France, 2022. The authors are grateful to the anonymous reviewers for their constructive comments on an earlier version of this manuscript.Disclosure statementNo potential conflict of interest was reported by the author(s).Data availability statementThe data that support the findings of this study are available from the corresponding author upon reasonable request.Additional informationFundingThis work was supported by the State of Hesse for energy subsidies within the scope of the Hessian Energy Act (Hessischen Energiegesetztes, HEG) of 9 October 2019 with funds from the State of Hesse and with the kind support of the House of Energy [grant number E/411/71632164].Notes on contributorsMarc FüchtenhansMarc Füchtenhans received B.Sc. and M.Sc. degrees in business mathematics from Technical University of Darmstadt in 2014 and 2018. Since 2018, he is a Research Associate and Ph.D. student at the Institute of Production and Supply Chain Management at Technical University of Darmstadt. His research interests include sustainable solutions in the context of production and supply chain management. His works have appeared in the International Journal of Production Research and the International Journal of Logistics Research and Applications, among others.Christoph H. GlockChristoph H. Glock is a full professor and head of the Institute of Production and Supply Chain Management at Technical University of Darmstadt. His research interests include inventory management, supply chain management, warehousing, sustainable production and human factors in logistics and inventory systems. He has published in renowned international journals, such as the European Journal of Operational Research, Decision Sciences, the International Journal of Production Economics, the International Journal of Production Research, Omega, Transportation Research Part E or IISE Transactions.","PeriodicalId":14307,"journal":{"name":"International Journal of Production Research","volume":"15 1","pages":"0"},"PeriodicalIF":7.0000,"publicationDate":"2023-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"The impact of incentive-based programmes on job-shop scheduling with variable machine speeds\",\"authors\":\"Marc Füchtenhans, Christoph H. 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We gain insights into how incentive-based programmes can be integrated into machine scheduling models and analyse the potential interdependencies and benefits that result from this integration.KEYWORDS: Job-shop schedulingenergy-efficient production planninggenetic algorithmsustainable manufacturingdemand response programmesincentive-based programmes AcknowledgementsThis paper is a revised and extended version of the conference paper ‘Energy-efficient job shop scheduling considering processing speed and incentive-based programmes’ that was presented at 10th IFAC Conference on Manufacturing Modelling, Management and Control in Nantes, France, 2022. The authors are grateful to the anonymous reviewers for their constructive comments on an earlier version of this manuscript.Disclosure statementNo potential conflict of interest was reported by the author(s).Data availability statementThe data that support the findings of this study are available from the corresponding author upon reasonable request.Additional informationFundingThis work was supported by the State of Hesse for energy subsidies within the scope of the Hessian Energy Act (Hessischen Energiegesetztes, HEG) of 9 October 2019 with funds from the State of Hesse and with the kind support of the House of Energy [grant number E/411/71632164].Notes on contributorsMarc FüchtenhansMarc Füchtenhans received B.Sc. and M.Sc. degrees in business mathematics from Technical University of Darmstadt in 2014 and 2018. Since 2018, he is a Research Associate and Ph.D. student at the Institute of Production and Supply Chain Management at Technical University of Darmstadt. 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引用次数: 0
摘要
摘要制造业对能源的需求量越来越大,可再生但易挥发的能源的使用越来越多,协调生产和能源供应变得越来越重要。在以激励为基础的计划的帮助下,电网运营商可以激励消费者在危急情况下调整电力需求,从而使电网的稳定不受威胁。在用户端,可以使用节能调度模型使能源消耗更加灵活。提出了一种以总能耗和总加权延迟同时最小化为目标的变转速双目标作业车间调度问题。我们使用遗传算法来求解模型,并推导出帕累托边界来分析两个冲突目标之间的权衡。我们深入了解了如何将基于激励的程序集成到机器调度模型中,并分析了这种集成所带来的潜在相互依赖性和收益。关键词:作业车间调度、节能生产计划、遗传算法、可持续制造、需求响应计划、基于激励的计划。本文是会议论文“考虑加工速度和基于激励的计划的节能作业车间调度”的修订和扩展版本,该会议论文于2022年在法国南特举行的第10届IFAC制造建模、管理和控制会议上发表。作者感谢匿名审稿人对本文早期版本的建设性意见。披露声明作者未报告潜在的利益冲突。数据可得性声明支持本研究结果的数据可根据通讯作者的合理要求获得。这项工作得到了黑森州在2019年10月9日《黑森州能源法》(Hessischen Energiegesetztes, HEG)范围内的能源补贴的支持,由黑森州提供资金,并得到了能源议院的大力支持[批准号E/411/71632164]。marc fchtenhans于2014年和2018年分别获得德国达姆施塔特工业大学商业数学学士和硕士学位。自2018年以来,他是达姆施塔特工业大学生产与供应链管理研究所的研究员和博士生。他的研究兴趣包括生产和供应链管理背景下的可持续解决方案。他的作品曾发表在《国际生产研究杂志》和《国际物流研究与应用杂志》等杂志上。Christoph H. Glock是达姆施塔特工业大学生产和供应链管理研究所的全职教授和负责人。他的研究兴趣包括库存管理、供应链管理、仓储、可持续生产以及物流和库存系统中的人为因素。他曾在著名的国际期刊上发表文章,如《欧洲运筹学杂志》、《决策科学》、《国际生产经济学杂志》、《国际生产研究杂志》、《Omega》、《运输研究Part E》或《IISE Transactions》。
The impact of incentive-based programmes on job-shop scheduling with variable machine speeds
AbstractGiven the high demand for energy in the manufacturing industry and the increasing use of renewable but volatile energy sources, it becomes increasingly important to coordinate production and energy availability. With the help of incentive-based programmes, grid operators can incentivise consumers to adjust power demand in critical situations such that grid stability is not threatened. On the consumer side, energy-efficient scheduling models can be used to make energy consumption more flexible. This paper proposes a bi-objective job-shop scheduling problem with variable machine speeds that aims on minimising the total energy consumption and total weighted tardiness simultaneously. We use a genetic algorithm to solve the model and derive Pareto frontiers to analyse the trade-off between both conflicting objectives. We gain insights into how incentive-based programmes can be integrated into machine scheduling models and analyse the potential interdependencies and benefits that result from this integration.KEYWORDS: Job-shop schedulingenergy-efficient production planninggenetic algorithmsustainable manufacturingdemand response programmesincentive-based programmes AcknowledgementsThis paper is a revised and extended version of the conference paper ‘Energy-efficient job shop scheduling considering processing speed and incentive-based programmes’ that was presented at 10th IFAC Conference on Manufacturing Modelling, Management and Control in Nantes, France, 2022. The authors are grateful to the anonymous reviewers for their constructive comments on an earlier version of this manuscript.Disclosure statementNo potential conflict of interest was reported by the author(s).Data availability statementThe data that support the findings of this study are available from the corresponding author upon reasonable request.Additional informationFundingThis work was supported by the State of Hesse for energy subsidies within the scope of the Hessian Energy Act (Hessischen Energiegesetztes, HEG) of 9 October 2019 with funds from the State of Hesse and with the kind support of the House of Energy [grant number E/411/71632164].Notes on contributorsMarc FüchtenhansMarc Füchtenhans received B.Sc. and M.Sc. degrees in business mathematics from Technical University of Darmstadt in 2014 and 2018. Since 2018, he is a Research Associate and Ph.D. student at the Institute of Production and Supply Chain Management at Technical University of Darmstadt. His research interests include sustainable solutions in the context of production and supply chain management. His works have appeared in the International Journal of Production Research and the International Journal of Logistics Research and Applications, among others.Christoph H. GlockChristoph H. Glock is a full professor and head of the Institute of Production and Supply Chain Management at Technical University of Darmstadt. His research interests include inventory management, supply chain management, warehousing, sustainable production and human factors in logistics and inventory systems. He has published in renowned international journals, such as the European Journal of Operational Research, Decision Sciences, the International Journal of Production Economics, the International Journal of Production Research, Omega, Transportation Research Part E or IISE Transactions.
期刊介绍:
The International Journal of Production Research (IJPR), published since 1961, is a well-established, highly successful and leading journal reporting manufacturing, production and operations management research.
IJPR is published 24 times a year and includes papers on innovation management, design of products, manufacturing processes, production and logistics systems. Production economics, the essential behaviour of production resources and systems as well as the complex decision problems that arise in design, management and control of production and logistics systems are considered.
IJPR is a journal for researchers and professors in mechanical engineering, industrial and systems engineering, operations research and management science, and business. It is also an informative reference for industrial managers looking to improve the efficiency and effectiveness of their production systems.