Digital Twin-Assisted Intelligent Logistics and Warehouse Automation
-
DOI:
https://doi.org/10.67228/30715636/IJETMR-2024PI4K7XPublished 06-04-2024
Digital Twin, Intelligent Logistics, Warehouse Automation, Industry 4.0, Artificial Intelligence, Internet of Things (IoT), Smart Warehouse, Cyber-Physical Systems, Predictive Analytics, Autonomous Robots Issue
Section
ArticlesHow to Cite
[1]I. P. K, “Digital Twin-Assisted Intelligent Logistics and Warehouse Automation”, IJETMR, vol. 7, no. 1, pp. 01–18, Jun. 2024, doi: 10.67228/30715636/IJETMR-2024PI4K7X.Abstract
As the world has moved into the fourth industrial revolution, the logistics and warehouses have become more and more part of a digital ecosystem, where intelligent technologies are helping companies to reach unprecedented levels of operational efficiency, flexibility and sustainability. Digital Twin (DT) technology has become a game-changer as it enables virtual representations of physical logistics assets, warehouse structures and processes to be created in real-time. Digital Twins, when supported by Artificial Intelligence (AI), the Internet of Things (IoT), cloud computing, and big data analytics, enable predictive monitoring, autonomous decision making, dynamic resource allocation and ongoing process optimisation. This paper provides a thorough overview of Digital Twin-driven intelligent logistics and warehouse automation, discusses recent developments in technology, implementation models, and their industrial applications. The study covers the challenges of embedding IoT connected sensors, autonomous mobile robots, cyber-physical systems, edge-cloud computing, and machine learning algorithms to improve the productivity, inventory control, route optimization, and predictive maintenance of warehouses. Additionally, the paper examines weaknesses of traditional WMS and uncovers existing problems and gaps in the field of scalability, interoperability, cyber security and real-time synchronization. The survey presented in this work shows the importance of Digital Twin as a crucial enabling technology for new generation smart warehouses and for the intelligent supply chain in Industry 5.0, as well as the opportunities for future research in autonomous logistics operations in Industry 5.0.
References
[1] M. Grieves and J. Vickers, “Digital Twin: Mitigating Unpredictable, Undesirable Emergent Behavior in Complex Systems,” in Transdisciplinary Perspectives on Complex Systems, Springer, 2017.
[2] F. Tao, H. Zhang, A. Liu, and A. Y. C. Nee, “Digital Twin in Industry: State-of-the-Art,” IEEE Transactions on Industrial Informatics, vol. 15, no. 4, pp. 2405–2415, 2019.
[3] W. Kritzinger, M. Karner, G. Traar, J. Henjes, and W. Sihn, “Digital Twin in Manufacturing: A Categorical Literature Review and Classification,” IFAC-PapersOnLine, vol. 51, no. 11, pp. 1016–1022, 2018.
[4] F. Tao and Q. Qi, “Make More Digital Twins,” Nature, vol. 573, pp. 490–491, 2019.
[5] F. Tao, M. Zhang, and A. Y. C. Nee, Digital Twin Driven Smart Manufacturing, Academic Press, 2019.
[6] E. VanDerHorn and S. Mahadevan, “Digital Twin: Generalization, Characterization and Implementation,” Decision Support Systems, vol. 145, 2021.
[7] L. Wright and S. Davidson, “How to Tell the Difference Between a Model and a Digital Twin,” Advanced Modeling and Simulation in Engineering Sciences, vol. 7, no. 13, 2020.
[8] M. Javaid, A. Haleem, and R. Suman, “Digital Twin Applications toward Industry 4.0: A Review,” Cognitive Robotics, vol. 3, pp. 71–92, 2023.
[9] A. Drissi Elbouzidi et al., “The Role of AI in Warehouse Digital Twins: Literature Review,” Applied Sciences, vol. 13, no. 11, 6746, 2023.
[10] T. V. Le and R. Fan, “Digital Twins for Logistics and Supply Chain Systems: Literature Review, Conceptual Framework, Research Potential, and Practical Challenges,” arXiv preprint arXiv:2311.17317, 2023.
[11] J. Zhang, A. Brintrup, A. Calinescu, E. Kosasih, and A. Sharma, “Supply Chain Digital Twin Framework Design: An Approach of Supply Chain Operations Reference Model and System of Systems,” arXiv preprint arXiv:2107.09485, 2021.
[12] J. Liu, W. Yeoh, Y. Qu, and L. Gao, “Blockchain-Based Digital Twin for Supply Chain Management: State-of-the-Art Review and Future Research Directions,” arXiv preprint arXiv:2202.03966, 2022.
[13] N. Zhang, R. Bahsoon, N. Tziritas, and G. Theodoropoulos, “A Digital Twin Approach for Adaptive Compliance in Cyber-Physical Systems: Case of Smart Warehouse Logistics,” arXiv preprint arXiv:2310.07116, 2023.
[14] M. Christopher, Logistics & Supply Chain Management, 5th ed., Pearson, 2016.
[15] H. Stadtler, “Supply Chain Management and Advanced Planning—Basics, Overview and Challenges,” European Journal of Operational Research, vol. 163, no. 3, pp. 575–588, 2005.
[16] D. Simchi-Levi, X. Chen, and J. Bramel, The Logic of Logistics, 3rd ed., Springer, 2014.
[17] Y. Wang, J. H. Han, and P. Beynon-Davies, “Understanding Blockchain Technology for Future Supply Chains,” Supply Chain Management: An International Journal, vol. 24, no. 1, pp. 62–84, 2019.
[18] M. Queiroz and S. Wamba, “Blockchain Adoption Challenges in Supply Chain,” International Journal of Information Management, vol. 52, 2020.
[19] S. Ivanov and A. Dolgui, “A Digital Supply Chain Twin for Managing the Disruption Risks and Resilience in the Era of Industry 4.0,” Production Planning & Control, vol. 32, no. 9, pp. 775–788, 2021.
[20] K. Baryannis, S. Validi, S. Dani, and G. Antoniou, “Supply Chain Risk Management and Artificial Intelligence: State of the Art and Future Research Directions,” International Journal of Production Research, vol. 57, no. 7, pp. 2179–2202, 2019.
[21] R. Sutton and A. Barto, Reinforcement Learning: An Introduction, 2nd ed., MIT Press, 2018.
[22] Y. LeCun, Y. Bengio, and G. Hinton, “Deep Learning,” Nature, vol. 521, pp. 436–444, 2015.
[23] I. Goodfellow, Y. Bengio, and A. Courville, Deep Learning, MIT Press, 2016.
[24] A. Krizhevsky, I. Sutskever, and G. Hinton, “ImageNet Classification with Deep Convolutional Neural Networks,” Communications of the ACM, vol. 60, no. 6, pp. 84–90, 2017.
[25] K. He, X. Zhang, S. Ren, and J. Sun, “Deep Residual Learning for Image Recognition,” in Proc. IEEE CVPR, 2016.
[26] J. Redmon and A. Farhadi, “YOLOv3: An Incremental Improvement,” arXiv preprint arXiv:1804.02767, 2018.
[27] A. Bochkovskiy, C.-Y. Wang, and H.-Y. Liao, “YOLOv4: Optimal Speed and Accuracy of Object Detection,” arXiv preprint arXiv:2004.10934, 2020.
[28] D. Silver et al., “Mastering the Game of Go with Deep Neural Networks and Tree Search,” Nature, vol. 529, pp. 484–489, 2016.
[29] P. Wurman, R. D’Andrea, and M. Mountz, “Coordinating Hundreds of Cooperative, Autonomous Vehicles in Warehouses,” AI Magazine, vol. 29, no. 1, pp. 9–20, 2008.
[30] J. Gu, M. Goetschalckx, and L. McGinnis, “Research on Warehouse Operation: A Comprehensive Review,” European Journal of Operational Research, vol. 177, no. 1, pp. 1–21, 2007.
[31] R. de Koster, T. Le-Duc, and K. Roodbergen, “Design and Control of Warehouse Order Picking: A Literature Review,” European Journal of Operational Research, vol. 182, no. 2, pp. 481–501, 2007.
[32] J. Boysen, S. de Koster, and F. Weidinger, “Warehousing in the E-Commerce Era: A Survey,” European Journal of Operational Research, vol. 277, no. 2, pp. 396–411, 2019.
[33] R. Roodbergen and I. Vis, “A Survey of Literature on Automated Guided Vehicles,” European Journal of Operational Research, vol. 170, no. 3, pp. 677–709, 2006.
[34] S. Garrido et al., “Path Planning for Mobile Robot Navigation Using Voronoi Diagram and Fast Marching,” Robotics and Autonomous Systems, vol. 59, no. 5, pp. 310–322, 2011.
[35] M. Wooldridge, An Introduction to MultiAgent Systems, 2nd ed., Wiley, 2009.
[36] K. Ashton, “That ‘Internet of Things’ Thing,” RFID Journal, 2009.
[37] L. Atzori, A. Iera, and G. Morabito, “The Internet of Things: A Survey,” Computer Networks, vol. 54, no. 15, pp. 2787–2805, 2010.
[38] F. Bonomi, R. Milito, J. Zhu, and S. Addepalli, “Fog Computing and Its Role in the Internet of Things,” in Proc. MCC Workshop, 2012.
[39] M. Satyanarayanan, “The Emergence of Edge Computing,” Computer, vol. 50, no. 1, pp. 30–39, 2017.
[40] Y. Lu, “Industry 4.0: A Survey on Technologies, Applications and Open Research Issues,” Journal of Industrial Information Integration, vol. 6, pp. 1–10, 2017.
[41] L. Monostori, “Cyber-Physical Production Systems: Roots, Expectations and R&D Challenges,” Procedia CIRP, vol. 17, pp. 9–13, 2014.
[42] J. Lee, B. Bagheri, and H.-A. Kao, “A Cyber-Physical Systems Architecture for Industry 4.0-Based Manufacturing Systems,” Manufacturing Letters, vol. 3, pp. 18–23, 2015.
[43] A. Gilchrist, Industry 4.0: The Industrial Internet of Things, Apress, 2016.
[44] M. Hermann, T. Pentek, and B. Otto, “Design Principles for Industrie 4.0 Scenarios,” in Proc. HICSS, 2016.
[45] M. Piccarozzi, L. Silvestri, C. Silvestri, and A. Ruggieri, “Roadmap to Industry 5.0: Enabling Technologies, Challenges, and Opportunities,” Technological Forecasting and Social Change, vol. 196, 2023.
[46] P. Fraga-Lamas and T. M. Fernández-Caramés, “Forging the Industrial Metaverse: Where Industry 5.0, IIoT, Edge Computing and Digital Twins Meet,” arXiv preprint arXiv:2303.03139, 2023.
[47] X. Wang et al., “The Survey on Multi-Source Data Fusion in Cyber-Physical-Social Systems,” Information Fusion, vol. 88, pp. 1–21, 2022.
[48] S. Peres et al., “Industrial Artificial Intelligence in Industry 4.0: Systematic Review, Challenges and Outlook,” IEEE Access, vol. 8, pp. 220121–220139, 2020.
[49] D. Alfaro-Viquez et al., “A Comprehensive Review of AI-Based Digital Twin Applications in Manufacturing,” Electronics, vol. 12, no. 15, 2023.
[50] I. Graessler and A. Pöhler, “Integration of a Digital Twin as Human Representation in a Scheduling Procedure of a Cyber-Physical Production System,” in Proc. IEEE IEEM, 2017.
[51] B. Gaffinet, J. A. H. Ali, Y. Naudet, and H. Panetto, “Human Digital Twins: A Systematic Literature Review and Concept Disambiguation for Industry 5.0,” Computers in Industry, vol. 150, 2023.
Downloads
How to Cite
[1]I. P. K, “Digital Twin-Assisted Intelligent Logistics and Warehouse Automation”, IJETMR, vol. 7, no. 1, pp. 01–18, Jun. 2024, doi: 10.67228/30715636/IJETMR-2024PI4K7X.