AI is used in last-mile delivery to optimize logistics operations, route planning, and delivery efficiency. It employs machine learning algorithms and real-time data analysis to streamline delivery processes, predict demand, and optimize delivery routes, leading to faster and more cost-effective last-mile deliveries.
This study delves into customer preferences for automated parcel delivery modes, including autonomous vehicles, drones, sidewalk robots, and bipedal robots, in the context of last-mile logistics. Using an Integrated Nested Choice and Correlated Latent Variable model, the research reveals that cost and time performance significantly influence the acceptability of technology, with a growing willingness to explore novel delivery automation when cost and time align.
A groundbreaking mathematical model, the FSTSP-DR-MP, has been proposed to transform last-mile logistics into a more sustainable and efficient process. With the surge in online shopping and the subsequent rise in carbon emissions, this innovative approach integrates both delivery and return services using a combination of trucks and drones. The model optimizes routes, considering multiple payloads and customers, to minimize service time.
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