ArticleSensors (Basel, Switzerland)2025
Research on Collaborative Delivery Path Planning for Trucks and Drones in Parcel Delivery.
Article in Sensors (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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3 authors.
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Abstract
With the rapid development of e-commerce, the logistics industry faces multiple challenges, including high delivery costs, long delivery times, and a shortage of delivery personnel. Truck-drone collaborative delivery combines the high load capacity of trucks with the flexibility and speed of drones, offering an innovative and practical solution. This paper proposes the Truck-Drone Collaborative Delivery Routing Problem (TDCRPTW) and develops a multi-objective optimization model that minimizes delivery costs and maximizes time reliability under capacity and time window constraints in multi-truck, multi-drone scenarios. To solve the model, an innovative two-stage solution strategy that combines the adaptive k-means++ clustering algorithm with temperature-controlled memory simulated annealing (TCMSA) is proposed. The experimental results demonstrate that the proposed model reduces delivery costs by 10% to 50% and reduces delivery time by 15% to 40%, showcasing the superiority of the truck-drone collaborative delivery model. Moreover, the proposed algorithm demonstrates outstanding performance and reliability across multiple dimensions. Therefore, the proposed approach provides an efficient solution to the truck-drone collaborative delivery problem and offers valuable insights for enhancing the efficiency and reliability of e-commerce logistics systems.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.