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PurposeElectric trucks and platooning are promising technologies to reduce greenhouse gas emissions in the freight sector. To maximize the benefits of these two technologies, effective coordination of charging and platooning is essential, especially considering insufficient charging stations (CSs), long charging duration and tight freight delivery window for middle-mile electric trucks. Therefore, this paper aims to jointly optimize the scheduling of charging and platooning of electric trucks over the freight transportation network. Design/methodology/approachThis paper proposes a mixed integer linear programming model to minimize the total costs from en-route charging, depot charging, and delivery delay. This also presents scenario analyses to understand the impacts of key features on system costs, including battery capacity, number of charging plugs at CSs, charging speed, availability of alternative paths and platoon energy-saving percentage. To solve the model with a large fleet size, a warm-start-based parameter-tuned solver approach, and hybrid metaheuristics of variable neighborhood search and local branching were implemented and compared based on performance. FindingsThe proposed model was implemented using the freight network in Florida. In a case study with a small fleet size, platoon scheduling reduced 19% of en-route charging cost and 30% of delivery delay cost compared with the case of only charge scheduling. Electric trucks were charged around three times with an average duration of 35 min per session to facilitate platoon scheduling and minimize the total cost. Originality/valuePrevious models optimized charging and platoon scheduling for single routes that cannot be generalized for network level and multiple origin-destination pairs; this study addresses network-level optimization.
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Cybersecurity Considerations for the Design of an AI-Driven Distributed Optimization of Container Carbon Emissions Reduction for Freight Operations
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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- Award ID(s):
- 2219773
- PAR ID:
- 10542103
- Editor(s):
- Daduna, J R; Liedtke, G; Shi, X; Voss, S
- Publisher / Repository:
- Springer Nature Switzerland
- Date Published:
- Volume:
- 14239
- ISSN:
- 1611-3349
- ISBN:
- 978-3-031-43612-3
- Page Range / eLocation ID:
- 56-84
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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