Abstract
AbstractThe existing logistics practices frequently lack the ability to effectively handle disruptions. Recent research called for dynamic, digital-driven approaches that can help prioritise allocation of logistics resources to design more adaptive and sustainable logistics networks. The purpose of this study is to explore inter-dependencies between physical and digital assets to examine how cyber-physical systems could enable interoperability in logistics networks. The paper provides an overview of the existing literature on cyber-physical applications in logistics and proposes a conceptual model of a Cloud Material Handling System. The model allows leveraging the use of digital technologies to capture and process real-time information about a logistics network with the aim to dynamically allocate material handling resources and promote asset and infrastructure sharing. The model describes how cloud computing, machine learning and real-time information can be utilised to dynamically allocate material handling resources to product flows. The adoption of the proposed model can increase efficiency, resilience and sustainability of logistics practices. Finally, the paper offers several promising research avenues for extending this work.
Funder
NTNU Norwegian University of Science and Technology
Publisher
Springer Science and Business Media LLC
Subject
Artificial Intelligence,Industrial and Manufacturing Engineering,Software
Reference124 articles.
1. Alarifi, A., Al-Salman, A., Alsaleh, M., et al. (2016). Ultra wideband indoor positioning technologies: analysis and recent advances. Sensors, 16(5), 707. https://doi.org/10.3390/s16050707
2. Alexopoulos, K., Nikolakis, N., & Chryssolouris, G. (2020). Digital twin-driven supervised machine learning for the development of artificial intelligence applications in manufacturing. International Journal of Computer Integrated Manufacturing, 33(5), 429–439. https://doi.org/10.1080/0951192X.2020.1747642
3. ALICE. (2020). Alliance for Logistics Innovation through Collaboration in Europe (ALICE). https://www.etp-logistics.eu/wp-content/uploads/2020/11/Roadmap-to-Physical-Intenet-Executive-Version_Final.pdf
4. Alves, J.C., Silva, DMD. & Mateus, GR. (2021). Applying and Comparing Policy Gradient Methods to Multi-echelon Supply Chains with Uncertain Demands and Lead Times. In: Artificial Intelligence and Soft Computing. Springer, Cham, Switzerland, p 229–239, https://doi.org/10.1007/978-3-030-87897-9_21
5. Araz, O. M., Choi, T. M., Olson, D. L., et al. (2020). Data Analytics for Operational Risk Management. Decision Sciences, 51(6), 1316–1319. https://doi.org/10.1111/deci.12443
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