Abstract
The development of Industrial Internet of Things, big data, and multi-domain modeling, led to the emergence of digital twin (DT), providing a new approach to the cyber-physical production systems. In the traditional shipbuilding industry, a large number of hull parts are often tracked and transferred. During the hull parts picking and processing, the uncertainty from parts transfer tasks and logistics information often leads to low resource utilization. Therefore, a real-time resource allocation method based on DT for hull part smart picking and processing system (SPPS) is proposed. Firstly, a multi-agent model of the multi-gantry crane system is established in virtual space to achieve real-time task allocation, hence minimizing the transport time. Next, a real-time picking and processing scheduling policy is proposed to reduce the idle time of all workstations by estimating the time of parts arriving at target station and processing completion time. Finally, the services available in the DT platform can be applied to optimize the system performance, taking the number of devices and the takt time optimization as examples. Several experiments in case study are carried out to verify the proposed method. The average utilization rate of all workstations is increased by 17.39%, and the standard deviation is reduced by 83.31%. The results have shown that the proposed method can effectively improve the workstation utilization rate and load balance. The maximum average number of parts in the station buffer, which is limited to 0.33, is kept at a low level.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Abbreviations
- AGVs:
-
Automated Guided Vehicles
- AML:
-
AutomationML
- ANN:
-
Artificial Neural Network
- BOM:
-
Bill Of Materials
- CAD:
-
Computer Aided Design
- DES:
-
Discrete Event Simulation
- FCFP:
-
First Come–First Picking
- GA:
-
Genetic Algorithm
- GUID:
-
Global Unique IDentifier
- IoT:
-
Internet of Things
- JSON:
-
JavaScript Object Notation
- MCTA:
-
Multi-gantry Crane Task Allocation
- NSGA-II:
-
Non-dominated Sorting Genetic Algorithm II
- OPC UA:
-
OLE (Object Linking and Embedding) for Process Control Unified Architecture
- PHM:
-
Prognostic and Health Management
- PSO:
-
Particle Swarm Optimization
- QR code:
-
Quick Response code
- RFID:
-
Radio Frequency Identification
- RPPS:
-
Real-time Picking and Process Scheduling
- XML:
-
EXtensible Markup Language
References
Abourraja, M. N., Oudani, M., Samiri, M. Y., Boudebous, D., Fazziki, A. E., Najib, M., Bouain, A., & Rouky, N. (2017). A multi-agent based simulation model for rail–rail transshipment: An engineering approach for gantry crane scheduling. IEEE Access, 5, 13142–13156. https://doi.org/10.1109/ACCESS.2017.2713246
Alipour, M., Zare Mehrjedrdi, Y., & Mostafaeipour, A. (2020). A rule-based heuristic algorithm for online order batching and scheduling in an order picking warehouse with multiple pickers. R.A.I.R.O. Recherche Opérationnelle, 54(1), 101–117. https://doi.org/10.1051/ro/2018069
Arrichiello, V., & Gualeni, P. (2019). Systems engineering and digital twin: A vision for the future of cruise ships design, production and operations. International Journal on Interactive Design and Manufacturing, 14(1), 115–122. https://doi.org/10.1007/s12008-019-00621-3
Basan, N. P., Achkar, V. G., Mendez, C. A., & Garcia-del-Valle, A. (2017). A heuristic simulation-based framework to improve the scheduling of blocks assembly and the production process in shipbuilding. In Winter simulation conference (WSC), 2017 (pp. 3218–3229). https://doi.org/10.1109/WSC.2017.8248040.
Chen, K., Lin, S., Hsiao, J., Liu, C., Molisch, A. F., & Fettweis, G. P. (2021). Wireless networked multirobot systems in smart factories. Proceedings of the IEEE, 109(4), 468–494. https://doi.org/10.1109/JPROC.2020.3033753
Chen, N., Wang, Z., & Wu, J. (2013). Simulation-based research on adjustment technology of ship block production plan. In IEEE international conference on information technology and applications, 2013 (pp. 259–262). https://doi.org/10.1109/ITA.2013.67.
Fang, Y., Peng, C., Lou, P., Zhou, Z., Hu, J., & Yan, J. (2019). Digital-twin-based job shop scheduling toward smart manufacturing. IEEE Transactions on Industrial Informatics, 15(12), 6425–6435. https://doi.org/10.1109/TII.2019.2938572
Fortunato da Costa, T. M., Telles da Silva, V., Lavoura dos Santos, G., Lopes Duarte Filho, N., Silva da Costa Botelho, S., & Menezes de Oliveira, V. (2017). Hotlog: An IoT-based embedded system for intelligent tracking in shipyards. In 43rd Annual conference of the IEEE Industrial Electronics Society, 2017 (pp. 3455–3459). https://doi.org/10.1109/IECON.2017.8216585.
Fraga-Lamas, P., Noceda-Davila, D., Fernández-Caramés, T. M., Díaz-Bouza, M. A., & Vilar-Montesinos, M. (2016). Smart pipe system for a Shipyard 4.0. Sensors (basel, Switzerland). https://doi.org/10.3390/s1612218
Guo, H., Chen, M., Mohamed, K., Qu, T., Wang, S., & Li, J. (2021). A digital twin-based flexible cellular manufacturing for optimization of air conditioner line. Journal of Manufacturing Systems, 58, 65–78. https://doi.org/10.1016/j.jmsy.2020.07.012
Jeong, D., Kim, D., Choi, T., & Seo, Y. (2020). A process-based modeling method for describing production processes of ship block assembly planning. Processes, 8(7), 880. https://doi.org/10.3390/pr8070880
Kim, H., Lee, J. G., Lee, S. S., & Park, J. H. (2003). A simulation-based shipbuilding system for evaluation of validity in design and manufacturing. In IEEE international conference on systems, 2003 (pp. 522–529). https://doi.org/10.1109/icsmc.2003.1243868.
Li, X., He, B., Zhou, Y., & Li, G. (2021). Multisource model-driven digital twin system of robotic assembly. IEEE Systems Journal, 15(1), 114–123. https://doi.org/10.1109/JSYST.2019.2958874
Lim, K. Y. H., Zheng, P., & Chen, C. (2019). A state-of-the-art survey of digital twin: Techniques, engineering product lifecycle management and business innovation perspectives. Journal of Intelligent Manufacturing, 31(6), 1313–1337. https://doi.org/10.1007/s10845-019-01512-w
Luo, J., & Shen, Y. (2015). Energy efficiency optimization of belt conveyor for material scheduling problem. In IEEE international conference on information and automation, 2015 (pp. 122–127). https://doi.org/10.1109/ICInfA.2015.7279270.
Luo, W., Hu, T., Ye, Y., Zhang, C., & Wei, Y. (2020). A hybrid predictive maintenance approach for CNC machine tool driven by digital twin. Robotics and Computer-Integrated Manufacturing, 65, 101974. https://doi.org/10.1016/j.rcim.2020.101974
Muter, I., & Oncan, T. (2021). Order batching and picker scheduling in warehouse order picking. IISE Transactions, 54(5), 435–447. https://doi.org/10.1080/24725854.2021.1925178
Mykoniatis, K., & Harris, G. A. (2021). A digital twin emulator of a modular production system using a data-driven hybrid modeling and simulation approach. Journal of Intelligent Manufacturing, 32(7), 1899–1911. https://doi.org/10.1007/s10845-020-01724-5
Negri, E., Fumagalli, L., & Macchi, M. (2017). A review of the roles of digital twin in CPS-based production systems. Procedia Manufacturing, 11, 939–948. https://doi.org/10.1016/j.promfg.2017.07.198
Negri, E., Pandhare, V., Cattaneo, L., Singh, J., Macchi, M., & Lee, J. (2020). Field-synchronized digital twin framework for production scheduling with uncertainty. Journal of Intelligent Manufacturing, 32(4), 1207–1228. https://doi.org/10.1007/s10845-020-01685-9
Ou, X., Chang, Q., Chakraborty, N., & Wang, J. (2017). Gantry scheduling for multi-gantry production system by online task allocation method. IEEE Robotics and Automation Letters, 2(4), 1848–1855. https://doi.org/10.1109/LRA.2017.2710259
Pang, T. Y., Pelaez-Restrepo, J. D., Cheng, C., Yasin, A., Lim, H., & Miletic, M. (2021). Developing a digital twin and digital thread framework for an ‘industry 4.0’ shipyard. Applied Sciences, 11(3), 1–23. https://doi.org/10.3390/app11031097
Ren, Z., Wan, J., & Deng, P. (2022). Machine-learning-driven digital twin for lifecycle management of complex equipment. IEEE Transactions on Emerging Topics in Computing, 10(1), 9–22. https://doi.org/10.1109/TETC.2022.3143346
Rosen, R., Fischer, J., & Boschert, S. (2019). Next generation digital twin: An ecosystem for mechatronic systems? IFAC-PapersOnLine, 52(15), 265–270. https://doi.org/10.1016/j.ifacol.2019.11.685
Ruiz, J. C. S., Bru, J. M., & Escoto, R. P. (2021). Smart digital twin for ZDM-based job-shop scheduling. In 2021 IEEE international workshop on metrology for Industry 4.0 and IoT, 2021 (pp. 510–515). https://doi.org/10.1109/MetroInd4.0IoT51437.2021.9488473.
Schroeder, G. N., Steinmetz, C., Rodrigues, R. N., Henriques, R. V. B., Rettberg, A., & Pereira, C. E. (2021). A methodology for digital twin modeling and deployment for Industry 4.0. Proceedings of the IEEE, 109(4), 556–567. https://doi.org/10.1109/JPROC.2020.3032444
Tamaki, H., Kitamura, S., & Murao, H. (2004). Simulation-based optimization model and metaheuristic solution of multiple crane scheduling problems. In 2004 IEEE international conference on systems, man and cybernetics, 2004 (pp. 1469–1474). https://doi.org/10.1109/ICSMC.2004.1399838.
Tao, F., & Qi, Q. (2019). Make more digital twins. Nature (London), 573(7775), 490–491. https://doi.org/10.1038/d41586-019-02849-1
Tao, F., & Zhang, M. (2017). Digital twin shop-floor: A new shop-floor paradigm towards smart manufacturing. IEEE Access, 5, 20418–20427. https://doi.org/10.1109/ACCESS.2017.2756069
Tao, F., Zhang, H., Liu, A., & Nee, A. Y. C. (2019). Digital twin in industry: State-of-the-art. IEEE Transactions on Industrial Informatics, 15(4), 2405–2415. https://doi.org/10.1109/TII.2018.2873186
Thevenin, S., Mebarki, N., & Chatellier, P. (2021). Dynamic scheduling of a picking robot with limited buffer and rejection: An industrial case study. International Transactions in Operational Research, 29(3), 1394–1416. https://doi.org/10.1111/itor.13050
Villalonga, A., Negri, E., Biscardo, G., Castano, F., Haber, R. E., Fumagalli, L., & Macchi, M. (2021). A decision-making framework for dynamic scheduling of cyber-physical production systems based on digital twins. Annual Reviews in Control, 51, 357–373. https://doi.org/10.1016/j.arcontrol.2021.04.008
Wan, J., Li, X., Dai, H., Kusiak, A., Martinez-Garcia, M., & Li, D. (2021). Artificial-intelligence-driven customized manufacturing factory: Key technologies, applications, and challenges. Proceedings of the IEEE, 109(4), 377–398. https://doi.org/10.1109/JPROC.2020.3034808
Wan, J., Tang, S., Li, D., Wang, S., Liu, C., Abbas, H., & Vasilakos, A. V. (2017). A manufacturing big data solution for active preventive maintenance. IEEE Transactions on Industrial Informatics, 13(4), 2039–2047. https://doi.org/10.1109/TII.2017.2670505
Wan, J., Yin, B., Li, D., Celesti, A., Tao, F., & Hua, Q. (2018). An ontology-based resource reconfiguration method for manufacturing cyber–physical systems. IEEE/ASME Transactions on Mechatronics, 23(6), 2537–2546. https://doi.org/10.1109/TMECH.2018.2814784
Wang, Y., & Wu, Z. (2020). Model construction of planning and scheduling system based on digital twin. International Journal of Advanced Manufacturing Technology, 109(7–8), 2189–2203. https://doi.org/10.1007/s00170-020-05779-9
Wang, Y., & Wu, Z. (2022). Digital twin-based production scheduling system for heavy truck frame shop. Proceedings of the Institution of Mechanical Engineers: Part C Journal of Mechanical Engineering Science, 236(4), 1931–1942. https://doi.org/10.1177/0954406220913306
Wang, Z., Sheu, J., Teo, C., & Xue, G. (2021). Robot scheduling for Mobile-Rack warehouses: Human-Robot coordinated order picking systems. Production and Operations Management, 31(1), 98–116. https://doi.org/10.1111/poms.13406
Wooldridge, M. J. (2009). An introduction to multiagent systems (2nd ed.). Wiley.
Xia, M., Li, T., Shu, T., Wan, J., de Silva, C. W., & Wang, Z. (2019). A two-stage approach for the remaining useful life prediction of bearings using deep neural networks. IEEE Transactions on Industrial Informatics, 15(6), 3703–3711. https://doi.org/10.1109/TII.2018.2868687
Yan, J., Liu, Z., Zhang, C., Zhang, T., Zhang, Y., & Yang, C. (2021). Research on flexible job shop scheduling under finite transportation conditions for digital twin workshop. Robotics and Computer-Integrated Manufacturing, 72, 102198. https://doi.org/10.1016/j.rcim.2021.102198
Yan, Q., Wang, H., & Wu, F. (2022). Digital twin-enabled dynamic scheduling with preventive maintenance using a double-layer Q-learning algorithm. Computers and Operations Research, 144, 105823. https://doi.org/10.1016/j.cor.2022.105823
Yan, R., Jiang, P., Li, W., Yan, J., & Wen, L. (2012). The simulation of the ship production design process based on hierarchical timed petri net. Advanced Materials Research, 544, 170–175. https://doi.org/10.4028/www.scientific.net/AMR.544.170
Yoshitake, H., Kamoshida, R., & Nagashima, Y. (2019). New automated guided vehicle system using real-time holonic scheduling for warehouse picking. IEEE Robotics and Automation Letters, 4(2), 1045–1052. https://doi.org/10.1109/LRA.2019.2894001
Yue, W., Sun, J., Liu, F., Yang, P., Han, M., & Feng, M. (2010). A novel multi-RTGC scheduling problem based on genetic algorithm. In 2010 7th International conference on service systems and service management, 2010 (pp. 1–6). https://doi.org/10.1109/ICSSSM.2010.5530237.
Zhang, H., Yan, Q., & Wen, Z. (2020). Information modeling for cyber–physical production system based on digital twin and AutomationML. The International Journal of Advanced Manufacturing Technology, 107(2), 1927–1945. https://doi.org/10.1007/s00170-020-05056-9
Zhang, H., Zhang, G., & Yan, Q. (2019). Digital twin-driven cyber–physical production system towards smart shop-floor. Journal of Ambient Intelligence and Humanized Computing, 10(11), 4439–4453. https://doi.org/10.1007/s12652-018-1125-4
Zhang, J., Deng, T., Jiang, H., Chen, H., Qin, S., & Ding, G. (2021a). Bi-level dynamic scheduling architecture based on service unit digital twin agents. Journal of Manufacturing Systems, 60, 59–79. https://doi.org/10.1016/j.jmsy.2021.05.007
Zhang, J., Wang, X., & Huang, K. (2018). On-line scheduling of order picking and delivery with multiple zones and limited vehicle capacity. Omega (Oxford), 79, 104–115. https://doi.org/10.1016/j.omega.2017.08.004
Zhang, M., Tao, F., & Nee, A. Y. C. (2021b). Digital twin enhanced dynamic job-shop scheduling. Journal of Manufacturing Systems, 58, 146–156. https://doi.org/10.1016/j.jmsy.2020.04.008
Zhao, N., Fu, Z., Sun, Y., Pu, X., & Luo, L. (2021). Digital-twin driven energy-efficient multi-crane scheduling and crane number selection in workshops. Journal of Cleaner Production, 336, 130175. https://doi.org/10.1016/j.jclepro.2021.130175
Zhao, X., Liu, N., Zhao, S., Wu, J., Zhang, K., & Zhang, R. (2019). Research on the work-rest scheduling in the manual order picking systems to consider human factors. Journal of Systems Science and Systems Engineering, 28(3), 344–355. https://doi.org/10.1007/s11518-019-5407-y
Zhong, W., Zhang, J., & Chen, W. (2007). A novel discrete particle swarm optimization to solve traveling salesman problem. In 2007 IEEE congress on evolutionary computation, 2007 (pp. 3283–3287). https://doi.org/10.1109/CEC.2007.4424894.
Funding
This work was supported by The Joint Fund of the National Natural Science Foundation of China and Guangdong Province (Grant Number No. U1801264).
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Wang, X., Hu, X. & Wan, J. Digital-twin based real-time resource allocation for hull parts picking and processing. J Intell Manuf 35, 613–632 (2024). https://doi.org/10.1007/s10845-022-02065-1
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10845-022-02065-1