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Joint Optimization of Fleet Scheduling and Charging Deploymentfor Port Electrification
Xiao, Peiyan
Xiao, Peiyan
Abstract
The electrification of heavy-duty vehicles (HDVs) in maritime ports represents a critical pathwaytoward achieving low-emission and sustainable logistics. However, conventional plug-in charginginfrastructures often cause prolonged vehicle downtime and reduce scheduling efficiency. Thisthesis develops a bi-level optimization framework to jointly determine fleet configuration, jobscheduling, and dynamic wireless charging (DWC) deployment for the Port of Virginia. The upperlevel minimizes long-term system cost in terms of Net Present Value (NPV), while the lower leveloptimizes daily operations through adaptive scheduling and energy management. A heuristicsolver integrating greedy construction and Adaptive Large Neighborhood Search (ALNS) isimplemented to address the mixed-integer and nonlinear nature of the problem. Numericalexperiments using prediction-based realistic port demand data demonstrate that the proposedframework achieves full task feasibility with 100% service rate and yields an annual NPV 20.58million USD. Results further indicate that, under current cost and efficiency assumptions, DWCdeployment offers limited economic benefit compared to conventional charging strategies.When the total coil length is limited to under 1km, the system cost drops by 68.61%, makingDWC installation a potentially attractive choice. Overall, the proposed framework effectively linksinfrastructure investment and operational decisions, providing a scalable tool for portelectrification planning and adaptable extensions toward resilient, sustainable smart ports.
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2025-12-06
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http://creativecommons.org/licenses/by-nc-sa/4.0/
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Data Science