Model construction of planning and scheduling system based on digital twin
The emergence of uncertainty factors will exert a strong influence on the accuracy of workshop scheduling. Digital twin’ technology and planning and scheduling are integrated to form a planning and scheduling system based on digital twin, which can effectively use the concept of twin to carry out al...
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| Published in | International journal of advanced manufacturing technology Vol. 109; no. 7-8; pp. 2189 - 2203 |
|---|---|
| Main Authors | , |
| Format | Journal Article |
| Language | English |
| Published |
London
Springer London
01.08.2020
Springer Nature B.V |
| Subjects | |
| Online Access | Get full text |
| ISSN | 0268-3768 1433-3015 |
| DOI | 10.1007/s00170-020-05779-9 |
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| Abstract | The emergence of uncertainty factors will exert a strong influence on the accuracy of workshop scheduling. Digital twin’ technology and planning and scheduling are integrated to form a planning and scheduling system based on digital twin, which can effectively use the concept of twin to carry out all-around management and control of the uncertain factors in production activities so that the planning and scheduling scheme in the production process can accurately guide the actual production. Firstly, the system establishes management and control mechanism of planning and scheduling system based on digital twin. Second, it establishes a digital twin model of the planning and scheduling system for the management and control mechanism. Finally, the key technology of the planning and scheduling system is expounded, which mainly includes the perception and collection of total production factor information and the planning and scheduling prediction. In order to verify the validity of the scheme, the planning and scheduling system suited for the enterprise is designed and developed in combination with the frame workshop. |
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| AbstractList | The emergence of uncertainty factors will exert a strong influence on the accuracy of workshop scheduling. Digital twin’ technology and planning and scheduling are integrated to form a planning and scheduling system based on digital twin, which can effectively use the concept of twin to carry out all-around management and control of the uncertain factors in production activities so that the planning and scheduling scheme in the production process can accurately guide the actual production. Firstly, the system establishes management and control mechanism of planning and scheduling system based on digital twin. Second, it establishes a digital twin model of the planning and scheduling system for the management and control mechanism. Finally, the key technology of the planning and scheduling system is expounded, which mainly includes the perception and collection of total production factor information and the planning and scheduling prediction. In order to verify the validity of the scheme, the planning and scheduling system suited for the enterprise is designed and developed in combination with the frame workshop. |
| Author | Wang, Yunrui Wu, Zhengli |
| Author_xml | – sequence: 1 givenname: Yunrui surname: Wang fullname: Wang, Yunrui organization: School of Mechanical Engineering, Xi’an University of Science and Technology – sequence: 2 givenname: Zhengli surname: Wu fullname: Wu, Zhengli email: 657547674@qq.com organization: School of Mechanical Engineering, Xi’an University of Science and Technology |
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| Cites_doi | 10.1080/00207543.2016.1157271 10.1016/j.cie.2014.09.019 10.1007/s00170-017-0567-8 10.1109/TII.2018.2873186 10.1080/10.1016/j.jnca.2010.06.001 10.1016/j.jmsy.2017.03.008 10.1109/ACCESS.2017.2756069 10.1007/s00170-012-4344-4 10.1109/1016/j.engappai.2016.10.013 10.1080/0740817X.2010.505123 10.1109/ICMLC.2008.4620553 10.1109/FUZZ.2003.1206631 10.1109/ETFA.2018.8502508 10.1080/0951192X.2017.1413252 10.1080/00207543.2010.539281 10.1007/s00170-017-0800-5 10.2514/6.2012-1818 |
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| References | Chang JH, Ming HT (2005) Using simulation model to integrate production plan and dispatching system. In: IEEE International Symposium on Semiconductor Manufacturing Conference Proceedings. IEEE, pp 95–98 AzadehANegahbanAMoghaddamMA hybrid computer simulation-artificial neural network algorithm for optimisation of scheduling rule selection in stochastic job shop scheduling problemsInt J Prod Res201250255156610.1080/00207543.2010.539281 Glaessgen E, Stargel D (2012) The digital twin paradigm for future NASA and US Air Force vehicles. In: Proceedings of the 53rd structures dynamics and materials conference. AIAA, pp 1–14 Qu S, Wang J, Jasperneite J (2018) Dynamic scheduling in large-scale stochastic processing networks for demand-driven manufacturing using distributed reinforcement learning. In: IEEE International Conference on Emerging Technologies and Factory Automation. IEEE, pp 433–440. https://doi.org/10.1109/ETFA.2018.8502508 Lin FT (2003) Constructing a job-shop scheduling model based on imprecise data. In: IEEE International Conference on Fuzzy Systems. IEEE, pp 1374–1379. https://doi.org/10.1109/FUZZ.2003.1206631 GözüEKarpatYUncertainty analysis of force coefficients during micromilling of titanium alloyInt J Adv Manuf Tech2017931–483985510.1007/s00170-017-0567-8 ZhangYWangWDuWQianCYangHColoured petri net-based active sensing system of real-time and multi-source manufacturing information for smart factoryInt J Adv Manuf Tech2018949–123427343910.1007/s00170-017-0800-5 AnYWYanHSLagrangean relaxation approach to joint optimization for production planning and scheduling of synchronous assembly linesInt J Prod Res201654226718673510.1080/00207543.2016.1157271 KongXLinCJiangYEfficient dynamic task scheduling in virtualized data centers with fuzzy predictionJ Netw Comput Appl20113441068107710.1080/10.1016/j.jnca.2010.06.001 HeWSunDScheduling flexible job shop problem subject to machine breakdown with route changing and right-shift strategiesInt J Adv Manuf Tech2013661–450151410.1007/s00170-012-4344-4 LuCGaoLLiXXiaoSA hybrid multi-objective grey wolf optimizer for dynamic scheduling in a real-world welding industryEng Appl Artif Intell201757617910.1109/1016/j.engappai.2016.10.013 JiWWangLBig data analytics based fault prediction for shop floor schedulingJ Manuf Syst20174318719410.1016/j.jmsy.2017.03.008 TaoFZhanHLiuANeeAYCDigital twin in industry: state-of-the-artIEEE T Ind Inform20181542405241510.1109/TII.2018.2873186 TaoFZhangMDigital twin shop-floor: a new shop-floor paradigm towards smart manufacturingIEEE ACCESS20175204182042710.1109/ACCESS.2017.2756069 Lei DM, Guo XP (2008) Solving fuzzy flexible job shop scheduling problems using genetic algorithm. In: Proceedings of the 7th International Conference on Machine Learning and Cybernetics. IEEE, pp 1014–1019. https://doi.org/10.1109/ICMLC.2008.4620553 ZhangYZhaoXSunSActive sensing and dynamic scheduling for IOT manufacturing2015BeijingScience Press ZhouLZhangLSarkerBRLiYLeiRAn event-triggered dynamic scheduling method for randomly arriving tasks in cloud manufacturingTnt J Comput Integ M201831331833310.1080/0951192X.2017.1413252 BriskornDLeungJPinedoMRobust scheduling on a single machine using time buffersIIE Trans201143638339810.1080/0740817X.2010.505123 LuoHFangJHuangGQReal-time scheduling for hybrid flowshop in ubiquitous manufacturing environmentComput Ind Eng201584122310.1016/j.cie.2014.09.019 YW An (5779_CR10) 2016; 54 F Tao (5779_CR3) 2018; 15 Y Zhang (5779_CR18) 2018; 94 D Briskorn (5779_CR8) 2011; 43 F Tao (5779_CR4) 2017; 5 C Lu (5779_CR9) 2017; 57 L Zhou (5779_CR11) 2018; 31 5779_CR13 Y Zhang (5779_CR17) 2015 A Azadeh (5779_CR14) 2012; 50 W Ji (5779_CR16) 2017; 43 5779_CR6 H Luo (5779_CR12) 2015; 84 5779_CR5 5779_CR2 X Kong (5779_CR15) 2011; 34 5779_CR1 W He (5779_CR7) 2013; 66 E Gözü (5779_CR19) 2017; 93 |
| References_xml | – reference: Chang JH, Ming HT (2005) Using simulation model to integrate production plan and dispatching system. In: IEEE International Symposium on Semiconductor Manufacturing Conference Proceedings. IEEE, pp 95–98 – reference: LuoHFangJHuangGQReal-time scheduling for hybrid flowshop in ubiquitous manufacturing environmentComput Ind Eng201584122310.1016/j.cie.2014.09.019 – reference: Qu S, Wang J, Jasperneite J (2018) Dynamic scheduling in large-scale stochastic processing networks for demand-driven manufacturing using distributed reinforcement learning. In: IEEE International Conference on Emerging Technologies and Factory Automation. IEEE, pp 433–440. https://doi.org/10.1109/ETFA.2018.8502508 – reference: ZhangYZhaoXSunSActive sensing and dynamic scheduling for IOT manufacturing2015BeijingScience Press – reference: ZhangYWangWDuWQianCYangHColoured petri net-based active sensing system of real-time and multi-source manufacturing information for smart factoryInt J Adv Manuf Tech2018949–123427343910.1007/s00170-017-0800-5 – reference: AnYWYanHSLagrangean relaxation approach to joint optimization for production planning and scheduling of synchronous assembly linesInt J Prod Res201654226718673510.1080/00207543.2016.1157271 – reference: ZhouLZhangLSarkerBRLiYLeiRAn event-triggered dynamic scheduling method for randomly arriving tasks in cloud manufacturingTnt J Comput Integ M201831331833310.1080/0951192X.2017.1413252 – reference: JiWWangLBig data analytics based fault prediction for shop floor schedulingJ Manuf Syst20174318719410.1016/j.jmsy.2017.03.008 – reference: Lei DM, Guo XP (2008) Solving fuzzy flexible job shop scheduling problems using genetic algorithm. In: Proceedings of the 7th International Conference on Machine Learning and Cybernetics. IEEE, pp 1014–1019. https://doi.org/10.1109/ICMLC.2008.4620553 – reference: TaoFZhangMDigital twin shop-floor: a new shop-floor paradigm towards smart manufacturingIEEE ACCESS20175204182042710.1109/ACCESS.2017.2756069 – reference: HeWSunDScheduling flexible job shop problem subject to machine breakdown with route changing and right-shift strategiesInt J Adv Manuf Tech2013661–450151410.1007/s00170-012-4344-4 – reference: GözüEKarpatYUncertainty analysis of force coefficients during micromilling of titanium alloyInt J Adv Manuf Tech2017931–483985510.1007/s00170-017-0567-8 – reference: AzadehANegahbanAMoghaddamMA hybrid computer simulation-artificial neural network algorithm for optimisation of scheduling rule selection in stochastic job shop scheduling problemsInt J Prod Res201250255156610.1080/00207543.2010.539281 – reference: Glaessgen E, Stargel D (2012) The digital twin paradigm for future NASA and US Air Force vehicles. In: Proceedings of the 53rd structures dynamics and materials conference. AIAA, pp 1–14 – reference: TaoFZhanHLiuANeeAYCDigital twin in industry: state-of-the-artIEEE T Ind Inform20181542405241510.1109/TII.2018.2873186 – reference: Lin FT (2003) Constructing a job-shop scheduling model based on imprecise data. In: IEEE International Conference on Fuzzy Systems. IEEE, pp 1374–1379. https://doi.org/10.1109/FUZZ.2003.1206631 – reference: KongXLinCJiangYEfficient dynamic task scheduling in virtualized data centers with fuzzy predictionJ Netw Comput Appl20113441068107710.1080/10.1016/j.jnca.2010.06.001 – reference: LuCGaoLLiXXiaoSA hybrid multi-objective grey wolf optimizer for dynamic scheduling in a real-world welding industryEng Appl Artif Intell201757617910.1109/1016/j.engappai.2016.10.013 – reference: BriskornDLeungJPinedoMRobust scheduling on a single machine using time buffersIIE Trans201143638339810.1080/0740817X.2010.505123 – volume: 54 start-page: 6718 issue: 22 year: 2016 ident: 5779_CR10 publication-title: Int J Prod Res doi: 10.1080/00207543.2016.1157271 – volume: 84 start-page: 12 year: 2015 ident: 5779_CR12 publication-title: Comput Ind Eng doi: 10.1016/j.cie.2014.09.019 – volume: 93 start-page: 839 issue: 1–4 year: 2017 ident: 5779_CR19 publication-title: Int J Adv Manuf Tech doi: 10.1007/s00170-017-0567-8 – ident: 5779_CR1 – volume: 15 start-page: 2405 issue: 4 year: 2018 ident: 5779_CR3 publication-title: IEEE T Ind Inform doi: 10.1109/TII.2018.2873186 – volume: 34 start-page: 1068 issue: 4 year: 2011 ident: 5779_CR15 publication-title: J Netw Comput Appl doi: 10.1080/10.1016/j.jnca.2010.06.001 – volume: 43 start-page: 187 year: 2017 ident: 5779_CR16 publication-title: J Manuf Syst doi: 10.1016/j.jmsy.2017.03.008 – volume: 5 start-page: 20418 year: 2017 ident: 5779_CR4 publication-title: IEEE ACCESS doi: 10.1109/ACCESS.2017.2756069 – volume: 66 start-page: 501 issue: 1–4 year: 2013 ident: 5779_CR7 publication-title: Int J Adv Manuf Tech doi: 10.1007/s00170-012-4344-4 – volume: 57 start-page: 61 year: 2017 ident: 5779_CR9 publication-title: Eng Appl Artif Intell doi: 10.1109/1016/j.engappai.2016.10.013 – volume: 43 start-page: 383 issue: 6 year: 2011 ident: 5779_CR8 publication-title: IIE Trans doi: 10.1080/0740817X.2010.505123 – ident: 5779_CR6 doi: 10.1109/ICMLC.2008.4620553 – ident: 5779_CR5 doi: 10.1109/FUZZ.2003.1206631 – ident: 5779_CR13 doi: 10.1109/ETFA.2018.8502508 – volume: 31 start-page: 318 issue: 3 year: 2018 ident: 5779_CR11 publication-title: Tnt J Comput Integ M doi: 10.1080/0951192X.2017.1413252 – volume: 50 start-page: 551 issue: 2 year: 2012 ident: 5779_CR14 publication-title: Int J Prod Res doi: 10.1080/00207543.2010.539281 – volume: 94 start-page: 3427 issue: 9–12 year: 2018 ident: 5779_CR18 publication-title: Int J Adv Manuf Tech doi: 10.1007/s00170-017-0800-5 – ident: 5779_CR2 doi: 10.2514/6.2012-1818 – volume-title: Active sensing and dynamic scheduling for IOT manufacturing year: 2015 ident: 5779_CR17 |
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| Title | Model construction of planning and scheduling system based on digital twin |
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