Design of Vehicle Scheduling for Last-Mile Fresh-Food Delivery Using A Data-Driven Approach
DOI:
https://doi.org/10.23055/ijietap.2025.32.3.10413Abstract
The continuously growing demand for fresh food in China is accompanied by a significant increase in delivery volume, which requires timely and efficient vehicle scheduling. To find optimal and practical solutions, we studied a vehicle routing problem for last-mile fresh-food delivery that incorporates both actual traffic time and customer time windows. Actual traffic data were collected and analyzed to forecast future traffic times. A data-driven optimization approach was designed to integrate data prediction and decision optimization models. Specifically, the support vector regression model and adaptive large neighborhood searching algorithm were employed to solve the data prediction problem and search for optimal decision solutions, respectively. Numerical experiments suggest that the proposed data-driven approach is highly applicable to last-mile delivery problems with time sensitivity, and the solutions found are of favorable practicality. In addition, an in-depth analysis of the impact of different prediction accuracies on the performance of decision optimization was conducted, suggesting that an unnecessarily high data prediction accuracy may not improve the overall performance of last-mile delivery.
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