A Data-Driven Approach to The City Last-Mile Delivery Problem Towards The Application of Shared Delivery Terminals
DOI:
https://doi.org/10.23055/ijietap.2024.31.6.10087Abstract
The application of shared delivery terminals is a promising trend in the development of last-mile delivery in city logistics because it can effectively improve the
The application of shared delivery terminals is a promising trend in the development of last-mile delivery in city logistics because it can effectively improve the delivery efficiency of couriers and relax the time-window restrictions for customers to pick up their parcels. In this study, we investigated a vehicle routing problem (VRP) for the application of shared delivery terminals in last-mile delivery. In practical delivery scenarios, the random storage and retrieval behaviors of customers can affect the usage of shared delivery terminals and lead to inevitable uncertainty regarding their available capacity, thereby increasing the complexity of last-mile delivery. To address this issue, we propose a VRP with stochastic terminal capacity (VRPSTC) and design a data-driven predictive optimization approach by collecting first-hand usage data on shared delivery terminals, forecasting the available capacity and optimizing the operational delivery schedule in practice. Numerical experiments show that the proposed data-driven approach can effectively solve the proposed VRPSTC and contribute to an approximately 17%–20% reduction in the total delivery cost compared with traditional stochastic optimization. The proposed VRPSTC is expected to enrich the concept of last-mile delivery in terms of both theoretical research and practical industrial applications.
efficiency of couriers and relax the time-window restrictions for customers to pick up their parcels. In this study, we investigated a vehicle routing problem (VRP) for the application of shared delivery terminals in last-mile delivery. In practical delivery scenarios, the random storage and retrieval behaviors of customers can affect the usage of shared delivery terminals and lead to inevitable uncertainty regarding their available capacity, thereby increasing the complexity of last-mile delivery. To address this issue, we propose a VRP with stochastic terminal capacity (VRPSTC) and design a data-driven predictive optimization approach by collecting first-hand usage data on shared delivery terminals, forecasting the available capacity and optimizing the operational delivery schedule in practice. Numerical experiments show that the proposed data-driven approach can effectively solve the proposed VRPSTC and contribute to an approximately 17%–20% reduction in the total delivery cost compared with traditional stochastic optimization. The proposed VRPSTC is expected to enrich the concept of last-mile delivery in terms of both theoretical research and practical industrial applications.
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