Multi-objective Human-robot collaborative batch scheduling in distributed hybrid flowshop via automatic design of local search-reconstruction-feedback algorithm
•The model of multi-objective DHFBSP_HC is constructed.•A configurable local search-reconstruction-feedback algorithm is developed.•An automated algorithm design is embedded into the algorithm designing. The emergence of distributed production models has spurred extensive research on distributed hyb...
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| Vydané v: | Computers & industrial engineering Ročník 203; s. 110983 |
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01.05.2025
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| ISSN: | 0360-8352 |
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| Abstract | •The model of multi-objective DHFBSP_HC is constructed.•A configurable local search-reconstruction-feedback algorithm is developed.•An automated algorithm design is embedded into the algorithm designing.
The emergence of distributed production models has spurred extensive research on distributed hybrid flowshop scheduling. Despite advancements in resource allocation for flowshops, most studies overlook human-robot collaboration, which remains essential for complex manufacturing processes in real-world production. Additionally, the rise of multi-variety, small-batch production has driven widespread adoption of batch scheduling. Therefore, this paper introduces a multi-objective distributed hybrid flowshop batch scheduling problem with human-robot collaboration (DHFBSP_HC), aiming to minimize makespan and total energy consumption simultaneously. To address this issue, we propose a local search-reconstruction-feedback (LSRF) algorithm, which consists of four core components: population initialization, local search, reconstruction, and feedback mechanism. Additionally, the algorithm incorporates three configurable strategies, including fitness evaluation approaches, initialization approaches, and objective normalization approaches. These configurable strategies are regarded as categorical parameters, whereas the other parameters are referred to as numerical parameters. To select categorical and numerical parameters that can optimize the results of multi-objective DHFBSP_HC, we introduce the automated algorithm design and use I/F-Race to optimize parameter settings. Through comparisons with several state-of-the-art algorithms, we demonstrate the effectiveness and superiority of the LSRF algorithm in solving the multi-objective DHFBSP_HC. |
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| AbstractList | •The model of multi-objective DHFBSP_HC is constructed.•A configurable local search-reconstruction-feedback algorithm is developed.•An automated algorithm design is embedded into the algorithm designing.
The emergence of distributed production models has spurred extensive research on distributed hybrid flowshop scheduling. Despite advancements in resource allocation for flowshops, most studies overlook human-robot collaboration, which remains essential for complex manufacturing processes in real-world production. Additionally, the rise of multi-variety, small-batch production has driven widespread adoption of batch scheduling. Therefore, this paper introduces a multi-objective distributed hybrid flowshop batch scheduling problem with human-robot collaboration (DHFBSP_HC), aiming to minimize makespan and total energy consumption simultaneously. To address this issue, we propose a local search-reconstruction-feedback (LSRF) algorithm, which consists of four core components: population initialization, local search, reconstruction, and feedback mechanism. Additionally, the algorithm incorporates three configurable strategies, including fitness evaluation approaches, initialization approaches, and objective normalization approaches. These configurable strategies are regarded as categorical parameters, whereas the other parameters are referred to as numerical parameters. To select categorical and numerical parameters that can optimize the results of multi-objective DHFBSP_HC, we introduce the automated algorithm design and use I/F-Race to optimize parameter settings. Through comparisons with several state-of-the-art algorithms, we demonstrate the effectiveness and superiority of the LSRF algorithm in solving the multi-objective DHFBSP_HC. |
| ArticleNumber | 110983 |
| Author | Zhang, Biao Wang, Qi Jiang, Xuchu He, Peng |
| Author_xml | – sequence: 1 givenname: Peng surname: He fullname: He, Peng email: hepeng81236@163.com organization: School of Computer Science, Liaocheng University, Liaocheng 252000, PR China – sequence: 2 givenname: Xuchu surname: Jiang fullname: Jiang, Xuchu email: xuchujiang@zuel.edu.cn organization: School of Statistics and Mathematics, Zhongnan University of Economics and Law, Wuhan 430073, PR China – sequence: 3 givenname: Qi orcidid: 0009-0009-5279-4272 surname: Wang fullname: Wang, Qi email: 202221130148@stu.zuel.edu.cn organization: School of Statistics and Mathematics, Zhongnan University of Economics and Law, Wuhan 430073, PR China – sequence: 4 givenname: Biao orcidid: 0000-0003-4148-8172 surname: Zhang fullname: Zhang, Biao email: zhangbiao@lcu-cs.com organization: School of Computer Science, Liaocheng University, Liaocheng 252000, PR China |
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| Cites_doi | 10.1109/TII.2023.3271749 10.1016/j.cor.2008.06.007 10.1007/s10489-022-03853-1 10.1007/s40747-023-01288-w 10.1016/j.swevo.2020.100804 10.1080/0305215X.2023.2198768 10.1016/j.amc.2017.01.004 10.1016/j.cie.2022.108672 10.1007/s10479-018-2969-x 10.1080/00207543.2022.2093680 10.1016/j.promfg.2020.01.354 10.1016/j.jmsy.2024.07.011 10.1109/TII.2021.3128405 10.1016/j.asoc.2024.111508 10.1080/00207543.2020.1780333 10.1016/j.jmsy.2023.09.017 10.1109/TETCI.2020.3022372 10.1016/j.eswa.2022.117256 10.1016/j.swevo.2024.101681 10.1016/j.eswa.2022.119151 10.1016/j.ejor.2009.09.024 10.1186/s10033-022-00683-7 10.1016/j.knosys.2021.107819 10.1109/TEVC.2021.3106168 10.1016/j.knosys.2023.110309 10.1109/TSMC.2021.3120702 10.1016/j.eswa.2021.115453 |
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| Keywords | Distributed hybrid flowshop scheduling Multi-objective optimization Automated algorithm design Human-robot collaborative scheduling |
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10.1016/j.cie.2025.110983_b0130 article-title: A Feedback-Based Artificial Bee Colony Algorithm for Energy-Efficient Flexible Flow Shop Scheduling Problem with Batch Processing Machines publication-title: Applied Soft Computing – volume: 183 year: 2021 ident: 10.1016/j.cie.2025.110983_b0105 article-title: Multi-Objective Evolutionary Algorithm Based on Multiple Neighborhoods Local Search for Multi-Objective Distributed Hybrid Flow Shop Scheduling Problem publication-title: Expert Systems with Applications doi: 10.1016/j.eswa.2021.115453 |
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| SubjectTerms | Automated algorithm design Distributed hybrid flowshop scheduling Human-robot collaborative scheduling Multi-objective optimization |
| Title | Multi-objective Human-robot collaborative batch scheduling in distributed hybrid flowshop via automatic design of local search-reconstruction-feedback algorithm |
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