This is the dataset for the project "Electric Truck Fleet Management Under Limited and Uncertain Charging Infrastructure Availability" (USC-DOT-1015). This dataset supports research on optimizing battery electric vehicle (BEV) fleet dispatching in last-mile freight logistics under uncertainty. It accompanies the study on the Electric Vehicle Routing Problem with Backhauls and Time Windows under Travel Time and Service Time Uncertainty (EVRPBTW-USUT), which extends previous research by incorporating a backhauling strategy and modeling uncertainty in travel and customer service times. The dataset consists of 60 benchmark instances, derived from the well-known EVRPTW dataset, with varying customer sizes and backhaul proportions, enabling robust evaluations of routing strategies. Additionally, a real-world dispatching dataset from a full-service supply chain company in San Bernardino County, California, is included to validate the approach in practical applications. Each instance is provided in CSV format, with detailed solutions recorded in Excel files. These datasets support the development and benchmarking of optimization algorithms, particularly for robust vehicle routing and sustainable urban freight logistics.
