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Cybersecurity Considerations for the Design of an AI-Driven Distributed Optimization of Container Carbon Emissions Reduction for Freight Operations

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Computational Logistics (ICCL 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14239))

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Abstract

The transportation industry is a vital component of the global economy, responsible for the movement of goods between different locations. The intermodal freight transportation system involves the use of different modes of transportation, such as trucks, trains, and ships, to move freight containers. However, this system is loaded with inefficiencies due to the poor availability of real-time coordination and disruptions, causing delays, increased costs, and thus, higher carbon emissions. AI has the potential to improve the intermodal freight transportation system’s efficiency by optimizing operations in real-time and self-evolving the models to make better/faster decisions. While both policymaking and business operations would benefit from using real-time optimization models, the implications and applications of these models are different in each context. In policymaking, real-time optimization models are used to improve public services, reduce overall network costs, and setting regulations for sustainable management of the network. The system can consider real-time traffic conditions, weather, and other factors to optimize the routing of the trucks, reducing transportation costs, improving delivery times, maintaining resiliency, and managing emissions. This work aims to contribute with a better understanding on how these information systems can be protected from cyberthreats, while performing the optimization of freight synchromodal transportation operations in real-time in terms of efficiency, cost-effectiveness, and carbon emissions reduction, considering the dynamic nature and heterogeneity of the intermodal freight system.

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Correspondence to Carlos Paternina-Arboleda .

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Paternina-Arboleda, C., Nestler, A., Kascak, N., Pour, M.S. (2023). Cybersecurity Considerations for the Design of an AI-Driven Distributed Optimization of Container Carbon Emissions Reduction for Freight Operations. In: Daduna, J.R., Liedtke, G., Shi, X., Voß, S. (eds) Computational Logistics. ICCL 2023. Lecture Notes in Computer Science, vol 14239. Springer, Cham. https://doi.org/10.1007/978-3-031-43612-3_4

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  • DOI: https://doi.org/10.1007/978-3-031-43612-3_4

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-43611-6

  • Online ISBN: 978-3-031-43612-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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