Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
skip to main content
review-article

Production logistics digital twins: : Research profiling, application, challenges and opportunities

Published: 01 December 2023 Publication History
  • Get Citation Alerts
  • Abstract

    In the era of Industry 4.0, Production Logistic Digital Twins (PLDTs) have garnered remarkable attention from both academic and industrial communities. This is evident from the growing number of research publications on PLDTs in international scientific journals and conferences. However, given the diversity and complexity of production logistics activities, there is a pressing need for systematic literature review to chart past research and identify potential directions for future endeavors. Therefore, this study primarily focuses on the application of Digital Twins (DTs) in Production Logistics (PL). Firstly, an analysis of PLDTs research profiling is carried out based on general trends, keywords, application scenarios, and basic functions. Secondly, the functional characteristics of PLDTs are examined while summarizing their advantages and limitations across various application scenarios such as transportation, packaging, warehousing, material distribution, and information processing. And the roles played by smart technologies such as Internet of Things (IoT) in PLDTs system are discussed. Finally, possible challenges and future directions of PLDTs in industrial application are presented, accompanied by appropriate classification and extensive recommendations.

    References

    [1]
    A. Kaiblinger, M. Woschank, State of the art and future directions of digital twins for production logistics: a systematic literature review, Appl. Sci. 12 (2) (2022) 669.
    [2]
    M. Krajcovic, P. Grznar, M. Fusko, et al., Intelligent logistics for intelligent production systems, Commun. Sci. Lett. Univ. Zilina 20 (4) (2018) 16–23.
    [3]
    V. Borisova, K. Taymashanov, Digital warehousing as a leading logistics potential, in: International Conference (Sustainable Leadership for Entrepreneurs and Academics), Prague, Springer Proceedings in Business and Economics AG, 2019, pp. 279–287.
    [4]
    F. Tao, Y. Cheng, J. Cheng, et al., Theories and technologies for cyber-physical fusion in digital twin shop-floor, Comput. Integr. Manuf. Syst. 23 (8) (2017) 1603–1611.
    [5]
    Y. Pan, T. Qu, N. Wu, et al., Digital twin based real-time production logistics synchronization system in a multi-level computing architecture, J. Manuf. Syst. 58 (2021) 246–260.
    [6]
    C. Piancastelli, M. Tucci, The role of digital twins in the fulfilment logistics chain, IFAC-PapersOnLine 53 (2) (2020) 10574–10578.
    [7]
    M. Thürer, S.S. L.i, T. Qu, Digital twin architecture for production logistics: the critical role of programmable logic controllers (PLCs), Procedia Comput. Sci. 200 (2022) 710–717.
    [8]
    P. Pawlewski, M. Kosacka-Olejnik, K. Werner-Lewandowska, Digital twin lean intralogistics: research implications, Appl. Sci. 11 (4) (2021) 1495.
    [9]
    J. Fottner, D. Clauer, F. Hormes, et al., Autonomous systems in intralogistics-state of the art and future research challenges, Logist. Res. 14 (2) (2021) 1–41.
    [10]
    M. Kosacka-Olejnik, M. Kostrzewski, M. Marczewska, et al., How digital twin concept supports internal transport systems? — Literature review, Energies 14 (16) (2021) 4919.
    [11]
    M. Zafarzadeh, M. Wiktorsson, J.B. H.auge, A systematic review on technologies for data-driven production logistics: their role from a holistic and value creation perspective, Logistics 5 (2) (2021) 24.
    [12]
    E. Marcucci, V. Gatta, M.Le Pira, et al., Digital twins: a critical discussion on their potential for supporting policy-making and planning in urban logistics, Sustainability 12 (24) (2020) 10623.
    [13]
    A.R. H.arish, X. Liu, R.Y. Z.hong, et al., Log-flock: a blockchain-enabled platform for digital asset valuation and risk assessment in e-commerce logistics financing, Comput. Ind. Eng. 151 (2021).
    [14]
    G. Miščević, E. Tijan, D. Žgaljić, et al., Emerging trends in e-logistics, in: Proc. 41st Int. Conv. Inf. Commun. Technol. Electron. Microelectron. (MIPRO), 2018, pp. 1353–1358.
    [15]
    L. Tebaldi, G. Vignali, E. Bottani, Digital twin in the agri-food supply chain: a literature review, IFIP Adv. Inf. Commun. Technol., Springer, Cham, 2021, pp. 276–283.
    [16]
    T. Defraeye, G. Tagliavini, W. Wu, et al., Digital twins probe into food cooling and biochemical quality changes for reducing losses in refrigerated supply chains, Resour. Conserv. Recycl. 149 (2019) 778–794.
    [17]
    S. Sani, D. Schaefer, J. Milisavljevic-Syed, Strategies for achieving pre-emptive resilience in military supply chains, Procedia CIRP 107 (2022) 1526–1532.
    [18]
    K.T. Park, Y. Son, S.D. Noh, The architectural framework of a cyber physical logistics system for digital-twin-based supply chain control, Int. J. Prod. Res. 59 (19) (2020) 5721–5742.
    [19]
    S.N. Grigoriev, V.A. Dolgov, P.A. Nikishechkin, et al., Development of a structural model of a digital twin of machine-building enterprises production and logistics system, Herald Bauman Moscow State Tech. Univ., Ser. Mech. Eng. (2) (2021) 137.
    [20]
    M. Li, D. Guo, G.Q. Huang, Operation twins: synchronized production-intralogistics for industry 4.0 manufacturing, IFIP Adv. Inf. Commun. Technol., Springer, Cham, 2021, pp. 596–604.
    [21]
    A.Z. Abideen, F.B. Mohamad, M.R. Hassan, Mitigation strategies to fight the COVID-19 pandemic—Present, future and beyond, J. Health Res. 34 (2020) 547–562.
    [22]
    A.Z. Abideen, V.P.K. Sundram, J. Pyeman, et al., Digital twin integrated reinforced learning in supply chain and logistics, Logistics 5 (4) (2021) 84.
    [23]
    J. Leng, D. Yan, Q. Liu, et al., Digital twin-driven joint optimisation of packing and storage assignment in large-scale automated high-rise warehouse product-service system, Int. J. Comput. Integr. Manuf. 34 (7–8) (2021) 783–800.
    [24]
    A. Ashrafian, O.G. Pettersen, K.N. Kuntze, et al., Full-scale discrete event simulation of an automated modular conveyor system for warehouse logistics, in: Proceedings of the IFIP International Conference on Advances in Production Management Systems, Austin, TX, USA, 1–5 September 2019, New York, NY, USA, Springer, 2019, pp. 35–42.
    [25]
    W. Wang, Y. Zhang, R.Y. Zhong, A proactive material handling method for CPS enabled shop-floor, Robotics Comput. Integr. Manuf. 61 (2020).
    [26]
    H. Jiang, T. Qu, M. Wan, et al., Digital-twin-based implementation framework of production service system for highly dynamic production logistics operation, IET Collab. Intell. Manuf. 2 (2) (2020) 74–80.
    [27]
    A. Villalonga, E. Negri, L. Fumagalli, et al., Local decision making based on distributed digital twin framework, IFAC-PapersOnLine 53 (2) (2020) 10568–10573.
    [28]
    F. Mostafa, L. Tao, W. Yu, An effective architecture of digital twin system to support human decision making and ai-driven autonomy, Concurr. Comput. Pract. Exp. 33 (19) (2021) e6111.
    [29]
    C.H. Dos Santos, J.a. De Queiroz, F. Leal, et al., Use of simulation in the industry 4.0 context: creation of a digital twin to optimise decision making on non-automated process, J. Simul. 16 (3) (2022) 284–297.
    [30]
    A. Murrenhoff, C. Pott, M. Wernecke, et al., Digital design of intralogistics systems: flexible and agile solution to short-cyclic fluctuations, in: 15th IMHRC Proceedings (Savannah, Georgia. USA –2018), 26, 2018.
    [31]
    T. Hiller, C. Cevirgen, P. Nyhuis, Exploring the potential of digital twins for production control & monitoring, J. Product. Sys. Logist. 1 (2021) (2021).
    [32]
    G.C. Perez, B. Korth, Digital twin for legal requirements in production and logistics based on the example of the storage of hazardous substances, in: Proceedings of the 2020 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM), Singapore, 2020, pp. 1093–1097. 14–17 December.
    [33]
    J.B. Hauge, M. Zafarzadeh, Y. Jeong, et al., Employing digital twins within production logistics, in: Proceedings of the 2020 IEEE International Conference on Engineering, Technology and Innovation (ICE/ITMC), Cardiff, UK, 15–17 June 2020, New York, NY, USA, IEEE Xplore, 2020, pp. 1–8.
    [34]
    C. Hegedűs, A. Frankó, P. Varga, Asset and production tracking through value chains for industry 4.0 using the arrowhead framework, in: Proceedings of the 2019 IEEE International Conference on Industrial Cyber Physical Systems (ICPS), Taipei, Taiwan, 2019, pp. 655–660. 6–9 May.
    [35]
    Y. Pan, N. Wu, T. Qu, et al., Digital-twin-driven production logistics synchronization system for vehicle routing problems with pick-up and delivery in industrial park, Int. J. Comput. Integr. Manuf. 34 (7–8) (2021) 814–828.
    [36]
    J.B. Hauge, M. Zafarzadeh, Y. Jeong, et al., Digital and physical testbed for production logistics operations, IFIP Adv. Inf. Commun. Technol. 591 (2020) 625–633.
    [37]
    R.S. Agostino, E. Broda, E.M. Frazzon, et al., Using a digital twin for production planning and control in industry 4.0, Int. Ser, Oper. Res. Manag. Sci. 289 (2020) 39–60.
    [38]
    L. Wang, Y. Wang, H. Yang, et al., Research on application of virtual-real fusion technology in smart manufacturing, in: Proceedings of the 2018 IEEE 9th International Conference on Software Engineering and Service Science, Los Alamitos, CA, IEEE, 2018, pp. 1066–1069.
    [39]
    F. Tao, C. Zhang, H. Zhang, et al., Future equipment exploration: digital twin equipment, Comput. Integr. Manuf. Syst. 28 (1) (2022) 1–16.
    [40]
    B. Hauge, M. Zafarzadeh, Y. Jeong, et al., Digital twin testbed and practical applications in production logistics with real-time location data, Int. J. Ind. Eng. Manag. 12 (2) (2021) 129–140.
    [41]
    W. Wu, Z. Zhao, L. Shen, et al., Just trolley: implementation of industrial IoT and digital twin-enabled spatial-temporal traceability and visibility for finished goods logistics, Adv. Eng. Inform. 52 (2022).
    [42]
    J.A. Marmolejo-Saucedo, Digital twin framework for large-scale optimization problems in supply chains: a case of packing problem, Mob. Netw. Appl. (2021) 1–17.
    [43]
    A. Dąbrowska, R. Giel, K. Winiarska, Sequencing and planning of packaging lines with reliability and digital twin concept considerations – a case study af a sugar production plant, Logforum 18 (03) (2022) 321–334.
    [44]
    D. Zhou, T. Qu, K. Zhang, et al., Digital twin driven decision-making architecture, model and method for synchronized production-transportation-storage system in industrial park, Comput. Integr. Manuf. Syst. 25 (06) (2019) 1576–1590.
    [45]
    J. Cao, A. Lei, Digital twin-driven warehouse management system for picking path planning problem, in: Proceedings of the International Conference on Computer Science, Engineering and Education Applications, Ningbo Tech University, China, 18-21 August 2022, Springer, Cham, 2022, pp. 3–16.
    [46]
    T. Petković, D. Puljiz, I. Marković, et al., Human intention estimation based on hidden markov model motion validation for safe flexible robotized warehouses, Robotics Comput. Integr. Manuf. 57 (2019) 182–196.
    [47]
    M. Bučková, R. Skokan, M. Fusko, et al., Designing of logistics systems with using of computer simulation and emulation, Transp. Res. Procedia 40 (2019) 978–985.
    [48]
    R. Harrison, Dynamically integrating manufacturing automation with logistics, in: 24th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA), IEEE, 2019, pp. 21–22.
    [49]
    R.A. I.ureva, N.S. K.uchenov, P.D. V.akhviyanova, Simulation of a modern assembly plant, in: 2019 International Multi-Conference on Industrial Engineering and Modern Technologies (FarEastCon), IEEE, 2019, pp. 1–6.
    [50]
    A. Barner, R. Neugebauer, M. Stratmann, et al., Innovationspotenziale der mensch-maschine-interaktion, Dossier, acatech, Berlin, 2016.
    [51]
    C. Tüllmann, M. Ten Hompel, A. Nettsträter, et al., Social networked industry ganzheitlich gestalten, Future challenges in logistics and supply chain management, Fraunhofer-Institut für Materialfluss und Logistik IML, Dortmund, 2017, Ausgabe 6.
    [52]
    E. Flores-García, Y. Jeong, M. Wiktorsson, et al., Digital twin-based services for smart production logistics, in: 2021 Winter Simulation Conference (WSC), IEEE, 2021, pp. 1–12.
    [53]
    L. Zhang, W. Liu, J. Cheng, et al., Just-in-time material distribution method for satellite assembly digital twin shop-floor, Comput. Integr. Manuf. Syst. 26 (11) (2020) 2897–2914.
    [54]
    M. Stan, T. Borangiu, S. Raileanu, Data-and model-driven digital twins for design and logistics control of product distribution, in: Proceedings of the 23rd International Conference on Control Systems and Computer Science Technologies, CSCS 2021, Bucharest, Romania, Institute of Electrical and Electronics Engineers Inc., 2021, pp. 33–40. Online Event. 26–28 May 2021.
    [55]
    S. Gallego-García, J. Reschke, M. García-García, Design and simulation of a capacity management model using a digital twin approach based on the viable system model: case study of an automotive plant, Appl. Sci. 9 (24) (2019) 5567.
    [56]
    Y. Lu, C. Liu, I. Kevin, et al., Digital twin-driven smart manufacturing: connotation, reference model, applications and research issues, Robotics Comput. Integr. Manuf. 61 (2020).
    [57]
    Y. Jeong, E. Flores-García, M. Wiktorsson, A design of digital twins for supporting decision-making in production logistics, in: Proceedings of the 2020 Winter Simulation Conference (WSC), Orlando, FL, USA, 2020, pp. 2683–2694. 14–18 December 2020.
    [58]
    W. Kuehn, Digital twins for decision making in complex production and logistic enterprises, Int. J. Des. Nat. Ecodyn. 13 (3) (2018) 260–271.
    [59]
    F. Tao, J. Cheng, Q. Qi, et al., Digital twin-driven product design, manufacturing and service with big data, Int. J. Adv. Manuf. Technol. 94 (9) (2018) 3563–3576.
    [60]
    M. Straka, R. Lenort, S. Khouri, et al., Design of large-scale logistics systems using computer simulation hierarchic structure, Int. J. Simul. Model. 17 (1) (2018) 105–118.
    [61]
    M. Andronie, G. Lăzăroiu, R. Ștefănescu, et al., Sustainable, smart, and sensing technologies for cyber-physical manufacturing systems: a systematic literature review, Sustainability 13 (10) (2021) 5495.
    [62]
    N.P. G.reis, M.L. N.ogueira, W. Rohde, Digital twin framework for machine learning-enabled integrated production and logistics processes, IFIP Adv. Inf. Commun. Technol. (2021) 218–227.
    [63]
    Z. Guo, Y. Zhang, X. Zhao, et al., CPS-based self-adaptive collaborative control for smart production-logistics systems, IEEE Trans. Cybern. 51 (1) (2020) 188–198.
    [64]
    A. Löcklin, T. Jung, N. Jazdi, et al., Architecture of a human-digital twin as common interface for operator 4.0 applications, Procedia CIRP 104 (2021) 458–463.
    [65]
    A. D'angelo, E.K.P. Chong, A systems engineering approach to incorporating the internet of things to reliability-risk modeling for ranking conceptual designs, in: ASME International Mechanical Engineering Congress and Exposition—Design, Reliability, Safety, and Risk, Pittsburgh, 2018, pp. 473–481.
    [66]
    B.S. Kim, S. Nam, Y. Jin, et al., Simulation framework for cyber-physical production system: applying concept of LVC interoperation, Complexity 2020 (2020).
    [67]
    P. Wang, W. Liu, N. Liu, et al., Digital twin-driven system for roller conveyor line: design and control, J. Ambient. Intell. Hum. Comput. 11 (11) (2020) 5419–5431.
    [68]
    D. Gyulai, J. Bergmann, A. Lengyel, et al., Simulation-based digital twin of a complex shop-floor logistics system, in: Proceedings of the 2020 Winter Simulation Conference (WSC), Orlando, FL, USA, 2020, pp. 1849–1860. 14–18 December.
    [69]
    A. Ait-Alla, M. Kreutz, D. Rippel, et al., Simulated-based methodology for the interface configuration of cyber-physical production systems, Int. J. Prod. Res. (7) (2020) 1–16.
    [70]
    Y. Bai, J.-B. You, I.-K. Lee, Design and optimization of smart factory control system based on digital twin system model, Math. Probl. Eng. 2021 (2021).
    [71]
    F. Spitzer, R. Froschauer, T. Schichl, ATLAS-A generic framework for generation of skill-based control logic and simulation models for intralogistics applications, in: 26th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA), IEEE, 2021, pp. 1–8.
    [72]
    D. Guo, R.Y. Z.hong, Y. Rong, et al., Synchronization of shop-floor logistics and manufacturing under IIoT and digital twin-enabled graduation intelligent manufacturing system, IEEE Trans. Cybern. (2021) 1–12.
    [73]
    J. Golova, K. Mahmood, T. Raamets, Simulation based performance analysis of production intralogistics, IOP Conf. Ser. 1140 (1) (2021).
    [74]
    M. Müller, J. Mielke, Y. Pavlovskyi, et al., Real-time combination of material flow simulation, digital twins of manufacturing cells, an AGV and a mixed-reality application, Procedia CIRP 104 (2021) 1607–1612.
    [75]
    P. Stączek, J. Pizoń, W. Danilczuk, et al., A digital twin approach for the improvement of an autonomous mobile robots (AMR's) operating environment—A case study, Sensors 21 (23) (2021) 7830.
    [76]
    A. Martínez-Gutiérrez, J. Díez-González, R. Ferrero-Guillén, et al., Digital twin for automatic transportation in industry 4.0, Sensors 21 (10) (2021) 3344.
    [77]
    J. Deng, S. Wei, X. Shi, et al., Digital twin-based distribution management system, Comput. Integr. Manuf. Syst. 27 (2) (2021) 585–604.
    [78]
    W. Wu, L. Shen, Z. Zhao, et al., Industrial IoT and long short-term memory network enabled genetic indoor tracking for factory logistics, IEEE Trans. Ind. Inform. (2022).
    [79]
    K.Y.H. Lim, L.V. Dang, C.-H. Chen, et al., Cost-optimal pathfinding model for multi-echelon logistics network design and optimization: a fourth-party logistics (4PL) perspective, in: 29th ISTE International Conference on Transdisciplinary Engineering, Cambridge, Advances in Transdisciplinary Engineering, TE 2022, 2022, pp. 473–481. 5 July 2022 - 8 July 2022.
    [80]
    L. Zheng, Y. Sun, H. Zhang, et al., Modeling and analysis of production logistics spatio-temporal graph network driven by digital twin, J. Donghua Univ. (Eng. Ed.) 39 (05) (2022) 461–474.
    [81]
    X. Sun, H. Yu, W.D. S.olvang, et al., The application of Industry 4.0 technologies in sustainable logistics: a systematic literature review (2012–2020) to explore future research opportunities, Environ. Sci. Pollut. Res. 29 (2021) 9560–9591.
    [82]
    Z. Xiao, S. Cheng, D. Zheng, et al., AGV path planning method for workshop driven by digital twin, Comput. Integr. Manuf. Syst. 28 (08) (2022) 2273–2290.
    [83]
    G. Avventuroso, M. Silvestri, P. Pedrazzoli, A networked production system to implement virtual enterprise and product lifecycle information loops, IFAC-PapersOnLine 50 (1) (2017) 7964–7969.
    [84]
    B. Korth, C. Schwede, M. Zajac, Simulation-ready digital twin for realtime management of logistics systems, in: Proceedings of the 2018 IEEE International Conference on Big Data (Big Data), Seattle, WA, USA, 2018, pp. 4194–4201. 10–13 December.
    [85]
    G. Bohács, D. Gáspár, D. Kánya, Conception an intelligent node architecture for intralogistics, Acta Logistica Int. Sci. J. Logist. 5 (2) (2018) 31–37.
    [86]
    Y. Ding, Brief analysis about digital twin supply chain model and application, Industrial Engineering and Innovation Management, 2, Clausius Scientific Press, Canada, 2019, pp. 14–23.
    [87]
    N. Nikolakis, K. Alexopoulos, E. Xanthakis, et al., The digital twin implementation for linking the virtual representation of human-based production tasks to their physical counterpart in the factory-floor, Int. J. Comput. Integr. Manuf. 32 (1) (2019) 1–12.
    [88]
    G. Guerreiro, P. Figueiras, R. Costa, et al., A digital twin for intra-logistics process planning for the automotive sector supported by big data analytics, in: ASME International Mechanical Engineering Congress and Exposition, Proceedings (IMECE), Volume 2B-2019, Salt Lake City, United States, 2019, 11-14 November.
    [89]
    S. Chen, W. Meng, W. Xu, et al., A warehouse management system with uav based on digital twin and 5 g technologies, in: 7th International Conference on Information, Cybernetics, and Computational Social Systems (ICCSS), IEEE, 2020, pp. 864–869.
    [90]
    K. Agalianos, S. Ponis, E. Aretoulaki, et al., Discrete event simulation and digital twins: review and challenges for logistics, Procedia Manuf. 51 (2020) 1636–1641.
    [91]
    S. Jagtap, F. Bader, G. Garcia-Garcia, et al., Food logistics 4.0: opportunities and challenges, Logistics 5 (1) (2021) 2.
    [92]
    P. Figueiras, L. Lourenço, R. Costa, et al., Big data provision for digital twins in industry 4.0 logistics processes, in: Proceedings of the 2021 IEEE International Workshop on Metrology for Industry 4.0 and IoT, MetroInd 4.0 and IoT 2021, Rome, Italy, 2021, pp. 516–521. 7–9 June.
    [93]
    W. Yao, J. Sun, S. Wu, Construction and simulation research of logistics automation stereoscopic storage system based on digital twin, in: 2nd International Conference on Applied Mathematics, Modelling, and Intelligent Computing (CAMMIC 2022), SPIE, 2022, pp. 848–852.
    [94]
    J. Guo, Z. Lv, Application of digital twins in multiple fields, Multimed. Tools Appl. 81 (2022) 26941–26967.
    [95]
    Y.-M. Tang, G. To Sum Ho, Y.-Y. Lau, et al., Integrated smart warehouse and manufacturing management with demand forecasting in small-scale cyclical industries, Machines 10 (6) (2022) 472.
    [96]
    Z. Zhang, Z. Zhu, J. Cheng, et al., Construction of intelligent integrated model framework for the workshop manufacturing system via digital twin, Int. J. Adv. Manuf. Technol. 118 (9) (2022) 3119–3132.
    [97]
    R. Ramirez, C.-Y. Huang, S.-H. Liang, 5G digital twin: a study of enabling technologies, Appl. Sci. 12 (15) (2022).
    [98]
    A. Ferrari, G. Zenezini, C. Rafele, et al., A roadmap towards an automated warehouse digital twin: current implementations and future developments, in: 10th IFAC Conference on Manufacturing Modelling, Management and Control, MIM 2022 Nantes 22 June 2022-24 June 2022, 2022, pp. 1899–1905.
    [99]
    A. Chakroun, Y. Hani, A. Elmhamedi, et al., Digital transformation process of a mechanical parts production workshop to fulfil the requirements of industry 4.0, 2022 14th International Colloquium of Logistics and Supply Chain Management (LOGISTIQUA), 2022, pp. 1–6.
    [100]
    A. Chakroun, Y. Hani, A. Elmhamedi, et al., A proposed integrated manufacturing system of a workshop producing brass accessories in the context of industry 4.0, Int. J. Adv. Manufact. Technol. (2022).
    [101]
    E. Badakhshan, P. Ball, Applying digital twins for inventory and cash management in supply chains under physical and financial disruptions, Int. J. Prod. Res. (2022) 1–23.
    [102]
    A. Venkatapathy, H. Bayhan, F. Zeidler, et al., Human machine synergies in intra-logistics: creating a hybrid network for research and technologies, in: Proceedings of the 2017 Federated Conference on Computer Science and Information Systems (FedCSIS), Prague, Czech Republic, 2017, pp. 1065–1068. 3–6 September.
    [103]
    H. Zhang, G. Zhang, Q. Yan, Dynamic resource allocation optimization for digital twin-driven smart shopfloor, in: Proceedings of the 2018 IEEE 15th International Conference on Networking, Sensing and Control (ICNSC), Zhuhai, China, 2018, pp. 1–5. 27–29 March.
    [104]
    O.Κ. Efthymiou, S.T. Ponis, Current status of industry 4.0 in material handling automation and in-house logistics, Int. J. Ind. Manuf. Eng. 13 (10) (2019) 1370–1374.
    [105]
    F. Coelho, S. Relvas, A.P. Barbosa-Póvoa, Simulation-based decision support tool for in-house logistics the basis for a digital twin, Comput. Ind. Eng. 153 (2021).
    [106]
    D.K. Lee, S. Song, H. Lee, Development and application of digital twin for the design verification and operation management of automated material handling systems, J. Comput. Des. Eng. 26 (4) (2021) 313–323.
    [107]
    G.-Y. Kim, E. Flores-García, M. Wiktorsson, et al., Exploring economic, environmental, and social sustainability impact of digital twin-based services for smart production logistics, in: Dolgui, A., Bernard, A., Lemoine, D., von Cieminski, G., Romero, D. (eds.) APMS 2021. IAICT, Springer, Cham, 634, 2021, pp. 20–27.
    [108]
    H. Guo, M. Chen, K. Mohamed, et al., A digital twin-based flexible cellular manufacturing for optimization of air conditioner line, J. Manuf. Syst. 58 (2021) 65–78.
    [109]
    J. Lei, J. Hui, K. Ding, et al., A framework for planning and scheduling shop floor logistics via cloud-edge collaboration, J. Phys. Conf. Ser. (2021).
    [110]
    Z. Zhao, M. Zhang, J. Chen, et al., Digital twin-enabled dynamic spatial-temporal knowledge graph for production logistics resource allocation, Comput. Ind. Eng. 171 (2022).
    [111]
    Y. Wang, Z. Jiang, Y. Wu, Model construction of material distribution system based on digital twin, Int. J. Adv. Manuf. Technol. 121 (7) (2022) 4485–4501.
    [112]
    C.S. Ko, H. Lee, T. Kim, Conceptual modeling for supply chain digital twin, ICIC Exp. Lett. Part B 13 (05) (2022) 495–501.
    [113]
    A. Follath, F. Bross, S. Galka, Process model for creating digital twins for production and logistics, J. Plast. Technol. 117 (10) (2022) 691–696.
    [114]
    J.-F. Uhlenkamp, J.B. H.auge, E. Broda, et al., Digital twins: a maturity model for their classification and evaluation, IEEE Access 10 (2022) 69605–69635.
    [115]
    W. Hofmann, S. Lang, P. Reichardt, et al., A brief introduction to deploy amazon web services for online discrete-event simulation, in: 3rd International Conference on Industry 4.0 and Smart Manufacturing, ISM 2021 Linz 19 November 2021-21 November 2021, 2022, pp. 386–393.
    [116]
    K.Y.H. Lim, L.V. Dang, C.-H. Chen, et al., Real-time postural training effects on single and multi-person ergonomic risk scores, in: 10th IFAC Conference on Manufacturing Modelling, Management and Control, MIM 2022 Nantes 22 June 2022-24 June 2022, 2022, pp. 163–168.
    [117]
    T. Nguyen, Q.H. Duong, T.V. Nguyen, et al., Knowledge mapping of digital twin and physical internet in supply chain management: a systematic literature review, Int. J. Prod. Econ. (44) (2022).
    [118]
    P. Zuhr, L. Rissmann, S. Meißner, Framework for planning and implementation of digital process twins in the field of internal logistics, in: 10th IFAC Conference on Manufacturing Modelling, Management and Control, MIM 2022 Nantes 22 June 2022-24 June 2022, 2022, pp. 2221–2227.
    [119]
    T. Borangiu, S. Răileanu, A smart palletising planning and control model in logistics 4.0 framework, Int. J. Prod. Res. (2022).
    [120]
    N. Berti, S. Finco, Digital twin and human factors in manufacturing and logistics systems: state of the art and future research directions, IFAC Pap. 55 (2022) 1893–1898.
    [121]
    X. Wang, X. Hu, J. Wan, Digital-twin based real-time resource allocation for hull parts picking and processing, J. Intell. Manuf. (2022).
    [122]
    M. Pivnička, D. Hrušecká, L. Hrbáčková, Introduction of a new flexible human resources planning system based on digital twin approach: a case study, Serb. J. Manag. 17 (02) (2022) 361–373.
    [123]
    M. G, A.R. M, A. Anbu, Digital twin framework for material handling and logistics in manufacturing part 1, in: 1st International Conference on Connected Systems and Intelligence, CSI 2022 Trivandrum 31 August 2022-2 September 2022 24 June 2022, 2022.
    [124]
    Y. Jeong, E. Flores-García, D.H. Kwak, et al., Digital twin-based services and data visualization of material handling equipment in smart production logistics environment, in: IFIP WG 5.7 International Conference on Advances in Production Management Systems, APMS 2022Gyeongju25 September 2022-29 September 2022, 2022, pp. 556–564.
    [125]
    D. Weigert, T. Reggelin, J. Tolujew, Material flow simulation of logistics processes-an approach of online analysis, planning and control of logistics processes of supply chains, in: The publications of the MultiScience - XXXI. MicroCAD International Scientific Conference, University of Miskolc, 2017, 20-21 April.
    [126]
    F. Zeidler, H. Bayhan, A.K.R. Venkatapathy, et al., Reference field for research and development of novel hybrid forms of human machine interaction in logistics, Logistics J. 2017 (2017).
    [127]
    B. Brenner, V. Hummel, Digital twin as enabler for an innovative digital shopfloor management system in the esb logistics learning factory at reutlingen-university, Procedia Manuf. 9 (2017) 198–205.
    [128]
    R. Furmann, B. Furmannova, D. Więcek, Interactive design of reconfigurable logistics systems, Procedia Eng. 192 (2017) 207–212.
    [129]
    D. Weigert, T. Lippke, T. Reggelin, et al., Simulation-based early warning system by coupling discrete event material flow simulation and artificial intelligence to support the operational phase of production and logistics systems, J. Ind. 4.0. (2023).
    [130]
    X. Li, J. Du, X. Wang, et al., Research on digital twin technology for production line design and simulation, in: 4th International Conference on Intelligent, Interactive Systems and Applications, Bangkok, Thailand, Springer, Cham, 2019, pp. 516–522.
    [131]
    W. Moritz, P. Christoph, M. Anike, et al., Innovative approach to participative planning of intralogistics systems-short-cycle and holistic planning of intralogistics systems using the digital design approach, in: 28th German Congress on Material Flow, 2019, pp. 57–76.
    [132]
    M. Schadler, N. Hafner, C. Landschützer, Konzepte und methoden für prädiktive instandhaltung in der intralogistik: concepts and methods for predictive maintenance in intralogistics, 15. Fachkolloquium Der Wissenschaftlichen Gesellschaft Für Technische Logistik, Wissenschaftliche Gesellschaft für Technische Logistik e. V., 2019, pp. 1–11.
    [133]
    L. Kavka, O. Kodym, M. Sedlacek, et al., Principles of industry 4.0 in teaching of logistics, Int. Multidiscip. Sci. Geo. Conf. SGEM Sofia 19 (5.4) (2019) 251–258.
    [134]
    V.A. Dolgov, V.E. Arkhangelskii, P.A. Nikishechkin, Method of analysis of production and logistics systems of discrete production based on product-process-resource model, external module for manufacturing control logic and simulation of work execution, in: Proceedings of the 2020 International Multi-Conference on Industrial Engineering and Modern Technologies (FarEastCon), Vladivostok, Russia, 2020, pp. 1–6. 6–9 October.
    [135]
    S.N. Grigoriev, V.A. Dolgov, P.A. Nikishechkin, et al., Information model of production and logistics systems of machine-building enterprises as the basis for the development and maintenance of their digital twins, in: Proceedings of the International Conference on Modern Trends in Manufacturing Technologies and Equipment (ICMTMTE), Sevastopol, Crimeea, 7–11 September 2020, Wales, UK, IOP Publishing, 2020, pp. 1–7.
    [136]
    E. Flores-García, G.-Y. Kim, J. Yang, et al., Analyzing the characteristics of digital twin and discrete event simulation in cyber physical systems, IFIP Adv. Inf. Commun. Technol., Springer, Cham, 2020, pp. 238–244.
    [137]
    M. Sommer, J. Stjepandić, S. Stobrawa, et al., Improvement of factory planning by automated generation of a digital twin, in: Proceedings of the Advances in Transdisciplinary Engineering, 12, Amsterdam, The Netherlands, IOS Press, 2020, pp. 453–462.
    [138]
    B. Nitsche, Exploring the potentials of automation in logistics and supply chain management: paving the way for autonomous supply chains, Logistics 5 (3) (2021) 51.
    [139]
    J. Vachálek, D. Šišmišová, P. Vašek, et al., Design and implementation of universal cyber-physical model for testing logistic control algorithms of production line's digital twin by using color sensor, Sensors 21 (5) (2021) 1842.
    [140]
    L. Xia, L. Jianfeng, H. Zhang, et al., A DTMEs-Based digital twin system construction method for smart factory, (2021).
    [141]
    M. Andronie, G. Lăzăroiu, M. Iatagan, et al., Sustainable cyber-physical production systems in big data-driven smart urban economy: a systematic literature review, Sustainability 13 (2) (2021) 751.
    [142]
    S. Pan, D. Trentesaux, D. Mcfarlane, et al., Digital interoperability and transformation in logistics and supply chain management, Comput. Ind. 129 (2021).
    [143]
    S. Pan, D. Trentesaux, D. Mcfarlane, et al., Digital interoperability in logistics and supply chain management: state-of-the-art and research avenues towards physical internet, Comput. Ind. 128 (2021).
    [144]
    Y.-H. Kuo, F. Pilati, T. Qu, et al., Digital twin-enabled smart industrial systems: recent developments and future perspectives, Int. J. Comput. Integr. Manuf. 34 (7–8) (2021) 685–689.
    [145]
    D. Malindžak, P. Kačmary, A. Gazda, et al., Order logistics for discrete and continual production processes in industry 4.0 conditions, Metalurgija 60 (1–2) (2021) 75–78.
    [146]
    D. Battini, N. Berti, S. Finco, et al., WEM-Platform: a real-time platform for full-body ergonomic assessment and feedback in manufacturing and logistics systems, Comput. Ind. Eng. 164 (2022).
    [147]
    Y. Wang, Z. Wu, Digital twin-based production scheduling system for heavy truck frame shop, in: Proc. Inst. Mech. Eng. Part C J. Mech. Eng. Sci., 2020, pp. 1–12.
    [148]
    N.P. Greis, M.L. Nogueira, W. Rohde, Towards learning-enabled digital twin with augmented reality for resilient production scheduling, in: 10th IFAC Conference on Manufacturing Modelling, Management and Control, MIM 2022 Nantes 22 June 2022-24 June 2022, 2022, pp. 1912–1917.
    [149]
    Y. Sun, J.-F. Klein, M. Sperling, et al., Development of an experimental environment to study the challenges in cyber-physical intralogistics systems, Logistics J. Proc. 2022 (11) (2022).
    [150]
    W. Ran, Y. Hu, Z. Yang, Application of digital twins to flexible production management taking a shandong factory as an example, Mobile Inform. Syst. (2022).
    [151]
    D. K, Dynamic simulation models as digital twins of logistics systems driven by data from multiple sources, in: 15th Global Congress on Manufacturing and Management, GCMM 2021 Liverpool 25 November 2020-27 November 2020, 2022.
    [152]
    L. Qiao, Y. Cheng, Analysis of logistics linkage by digital twins technology and lightweight deep learning, Comput. Intel. Neurosc. 2022 (2022).
    [153]
    C. Liu, H. Zhu, D. Tang, et al., Probing an intelligent predictive maintenance approach with deep learning and augmented reality for machine tools in IoT-enabled manufacturing, Robotics Comput. Integr. Manuf. 77 (2022).
    [154]
    M. Shafto, Modeling, simulation, Information Technology and Processing Roadmap, NASA, Washington, DC, USA, 2012.
    [155]
    H. Haße, B. Li, N. Weißenberg, et al., Digital twin for real-time data processing in logistics, in: Artificial Intelligence and Digital Transformation in Supply Chain Management: Innovative Approaches for Supply Chains. Proceedings of the Hamburg International Conference of Logistics (HICL), Hamburg, Germany, Technische Universität Hamburg, 2019, pp. 4–28.
    [156]
    F. Tao, M. Zhang, Y. Liu, et al., Digital twin driven prognostics and health management for complex equipment, CIRP Ann. 67 (1) (2018) 169–172.
    [157]
    F. Tao, B. Xiao, Q. Qi, et al., Digital twin modeling, J. Manuf. Syst. 64 (2022) 372–389.
    [158]
    F. Tao, H. Zhang, Q. Qi, et al., Theory of digital twin modeling and its application, Comput. Integr. Manuf. Syst. 27 (1) (2021) 1–15.
    [159]
    F. Tao, C. Zhang, Q. Qi, et al., Digital twin maturity model, Comput. Integr. Manuf. Syst. 28 (05) (2022) 1267–1281.
    [160]
    X. Zhang, Y. Zhang, J. Liu, et al., Research on digital maturity model of supply chain based on digital twin, Chn. Market (14) (2022) 113–116.
    [161]
    J. Wang, L. Zhang, K.-Y. Lin, et al., A digital twin modeling approach for smart manufacturing combined with the unison framework, Comput. Ind. Eng. (2022).
    [162]
    R. Bian, Z. Jia, W. Bi, et al., Fmea analysis of mechanical system of three-dimensional material warehouse unit, J. Beijing Univ. Aeronaut. Astronaut. 48 (1) (2022) 156–165.
    [163]
    T. Li, X. Si, X. Liu, et al., Data-model interactive remaining useful life prediction technologies for stochastic degrading devices with big data, Acta Automat. Sin. 45 (2021) 1–23.
    [164]
    Y. Wang, F. Tao, M. Zhang, et al., Digital twin enhanced fault prediction for the autoclave with insufficient data, J. Manuf. Syst. 60 (1) (2021) 350–359.
    [165]
    M. Xia, H. Shao, D. Williams, et al., Intelligent fault diagnosis of machinery using digital twin-assisted deep transfer learning, Reliab. Eng. Syst. Safe. 215 (2021).
    [166]
    W. Luo, T. Hu, Y. Ye, et al., A hybrid predictive maintenance approach for CNC machine tool driven by digital twin, Robotics Comput. Integr. Manuf. 65 (2020).

    Cited By

    View all

    Recommendations

    Comments

    Information & Contributors

    Information

    Published In

    cover image Robotics and Computer-Integrated Manufacturing
    Robotics and Computer-Integrated Manufacturing  Volume 84, Issue C
    Dec 2023
    313 pages

    Publisher

    Pergamon Press, Inc.

    United States

    Publication History

    Published: 01 December 2023

    Author Tags

    1. Digital twins
    2. Production logistics
    3. Production logistics digital twins

    Author Tags

    1. PLDTs
    2. DTs
    3. PL
    4. IoT
    5. PLS
    6. LDT
    7. PLAs
    8. CC
    9. CPS
    10. AGV
    11. AR
    12. AMR
    13. ST
    14. MR
    15. IIoT
    16. PLE
    17. DES
    18. VR
    19. RFID
    20. ML
    21. AI
    22. RL
    23. DL

    Qualifiers

    • Review-article

    Contributors

    Other Metrics

    Bibliometrics & Citations

    Bibliometrics

    Article Metrics

    • Downloads (Last 12 months)0
    • Downloads (Last 6 weeks)0

    Other Metrics

    Citations

    Cited By

    View all

    View Options

    View options

    Get Access

    Login options

    Media

    Figures

    Other

    Tables

    Share

    Share

    Share this Publication link

    Share on social media