Large-Scale Customized Production Scheduling of Multiagent-Based Medical 3D Printing
Three-dimensional (3D) printing, also known as additive manufacturing, has unique advantages over traditional manufacturing technologies; thus, it has attracted widespread attention in the medical field. Especially in the context of the frequent occurrence of major public health events, where the me...
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| Veröffentlicht in: | Computational intelligence and neuroscience 2022-07, Vol.2022, p.6557137-13 |
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| creator | He, Jianjia Wu, Jian Zhang, Ye Wang, Yaopeng He, Hua |
| description | Three-dimensional (3D) printing, also known as additive manufacturing, has unique advantages over traditional manufacturing technologies; thus, it has attracted widespread attention in the medical field. Especially in the context of the frequent occurrence of major public health events, where the medical industry’s demand for large-scale and customized production is increasing, traditional 3D printing production scheduling methods take a long time to handle large-scale customized medical 3D printing (M-3DP) production and have weak intelligent collaboration ability in the face of job-to-device matching under multimaterial printing. Given the problem caused by M-3DP large-scale customized production scheduling, an intelligent collaborative scheduling multiagent-based method is proposed in this study. First, a multiagent-based optimization model is established. On this basis, an improved genetic algorithm embedded with the product mix strategy and the intelligent matching mechanism is designed to optimize the completion time and load balance between devices. Finally, the effectiveness of the proposed method is evaluated using numerical simulation. The simulation results indicated that compared with the simple genetic algorithm, particle swarm optimization, and snake optimizer, the improved genetic algorithm could better reduce the M-3DP mass customization production scheduling time, optimize the load balance between devices, and promote the “intelligent manufacturing” process of M-3DP mass customization. |
| doi_str_mv | 10.1155/2022/6557137 |
| format | Article |
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Especially in the context of the frequent occurrence of major public health events, where the medical industry’s demand for large-scale and customized production is increasing, traditional 3D printing production scheduling methods take a long time to handle large-scale customized medical 3D printing (M-3DP) production and have weak intelligent collaboration ability in the face of job-to-device matching under multimaterial printing. Given the problem caused by M-3DP large-scale customized production scheduling, an intelligent collaborative scheduling multiagent-based method is proposed in this study. First, a multiagent-based optimization model is established. On this basis, an improved genetic algorithm embedded with the product mix strategy and the intelligent matching mechanism is designed to optimize the completion time and load balance between devices. Finally, the effectiveness of the proposed method is evaluated using numerical simulation. The simulation results indicated that compared with the simple genetic algorithm, particle swarm optimization, and snake optimizer, the improved genetic algorithm could better reduce the M-3DP mass customization production scheduling time, optimize the load balance between devices, and promote the “intelligent manufacturing” process of M-3DP mass customization.</description><identifier>ISSN: 1687-5265</identifier><identifier>ISSN: 1687-5273</identifier><identifier>EISSN: 1687-5273</identifier><identifier>DOI: 10.1155/2022/6557137</identifier><identifier>PMID: 35898774</identifier><language>eng</language><publisher>United States: Hindawi</publisher><subject>3-D printers ; 3D printing ; Additive manufacturing ; Algorithms ; Alliances ; Collaboration ; Completion time ; Computer Simulation ; COVID-19 ; Customization ; Genetic algorithms ; Heuristic ; Intelligent manufacturing systems ; Linear programming ; Load balancing ; Matching ; Mathematical models ; Mathematical optimization ; Medical equipment ; Multiagent systems ; Numerical analysis ; Optimization algorithms ; Optimization models ; Particle swarm optimization ; Printing ; Printing, Three-Dimensional ; Production methods ; Production scheduling ; Public health ; Scheduling ; Snakes ; Three dimensional printing</subject><ispartof>Computational intelligence and neuroscience, 2022-07, Vol.2022, p.6557137-13</ispartof><rights>Copyright © 2022 Jianjia He et al.</rights><rights>COPYRIGHT 2022 John Wiley & Sons, Inc.</rights><rights>Copyright © 2022 Jianjia He et al. This is an open access article distributed under the Creative Commons Attribution License (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. https://creativecommons.org/licenses/by/4.0</rights><rights>Copyright © 2022 Jianjia He et al. 2022</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c476t-25447abf038119420ee08a91ae7827d6c6eb22d40c4b7c196366d506854d2e53</citedby><cites>FETCH-LOGICAL-c476t-25447abf038119420ee08a91ae7827d6c6eb22d40c4b7c196366d506854d2e53</cites><orcidid>0000-0001-5987-3605 ; 0000-0002-6383-418X ; 0000-0003-0562-0711 ; 0000-0001-9783-0251</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC9313918/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC9313918/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,727,780,784,885,27924,27925,53791,53793</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/35898774$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><contributor>Bibbo, Daniele</contributor><contributor>Daniele Bibbo</contributor><creatorcontrib>He, Jianjia</creatorcontrib><creatorcontrib>Wu, Jian</creatorcontrib><creatorcontrib>Zhang, Ye</creatorcontrib><creatorcontrib>Wang, Yaopeng</creatorcontrib><creatorcontrib>He, Hua</creatorcontrib><title>Large-Scale Customized Production Scheduling of Multiagent-Based Medical 3D Printing</title><title>Computational intelligence and neuroscience</title><addtitle>Comput Intell Neurosci</addtitle><description>Three-dimensional (3D) printing, also known as additive manufacturing, has unique advantages over traditional manufacturing technologies; thus, it has attracted widespread attention in the medical field. Especially in the context of the frequent occurrence of major public health events, where the medical industry’s demand for large-scale and customized production is increasing, traditional 3D printing production scheduling methods take a long time to handle large-scale customized medical 3D printing (M-3DP) production and have weak intelligent collaboration ability in the face of job-to-device matching under multimaterial printing. Given the problem caused by M-3DP large-scale customized production scheduling, an intelligent collaborative scheduling multiagent-based method is proposed in this study. First, a multiagent-based optimization model is established. On this basis, an improved genetic algorithm embedded with the product mix strategy and the intelligent matching mechanism is designed to optimize the completion time and load balance between devices. Finally, the effectiveness of the proposed method is evaluated using numerical simulation. The simulation results indicated that compared with the simple genetic algorithm, particle swarm optimization, and snake optimizer, the improved genetic algorithm could better reduce the M-3DP mass customization production scheduling time, optimize the load balance between devices, and promote the “intelligent manufacturing” process of M-3DP mass customization.</description><subject>3-D printers</subject><subject>3D printing</subject><subject>Additive manufacturing</subject><subject>Algorithms</subject><subject>Alliances</subject><subject>Collaboration</subject><subject>Completion time</subject><subject>Computer Simulation</subject><subject>COVID-19</subject><subject>Customization</subject><subject>Genetic algorithms</subject><subject>Heuristic</subject><subject>Intelligent manufacturing systems</subject><subject>Linear programming</subject><subject>Load balancing</subject><subject>Matching</subject><subject>Mathematical models</subject><subject>Mathematical 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Especially in the context of the frequent occurrence of major public health events, where the medical industry’s demand for large-scale and customized production is increasing, traditional 3D printing production scheduling methods take a long time to handle large-scale customized medical 3D printing (M-3DP) production and have weak intelligent collaboration ability in the face of job-to-device matching under multimaterial printing. Given the problem caused by M-3DP large-scale customized production scheduling, an intelligent collaborative scheduling multiagent-based method is proposed in this study. First, a multiagent-based optimization model is established. On this basis, an improved genetic algorithm embedded with the product mix strategy and the intelligent matching mechanism is designed to optimize the completion time and load balance between devices. Finally, the effectiveness of the proposed method is evaluated using numerical simulation. 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| subjects | 3-D printers 3D printing Additive manufacturing Algorithms Alliances Collaboration Completion time Computer Simulation COVID-19 Customization Genetic algorithms Heuristic Intelligent manufacturing systems Linear programming Load balancing Matching Mathematical models Mathematical optimization Medical equipment Multiagent systems Numerical analysis Optimization algorithms Optimization models Particle swarm optimization Printing Printing, Three-Dimensional Production methods Production scheduling Public health Scheduling Snakes Three dimensional printing |
| title | Large-Scale Customized Production Scheduling of Multiagent-Based Medical 3D Printing |
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