Routing short-haul trucks under the uncertainties of travel time and service time

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.