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- W4367185959 abstract "Battery-powered drones or unmanned aerial vehicles (UAVs) offer significant market potential, enabling fast, cost-effective, and eco-friendly delivery on an urban scale. Numerous corporations are investing in initiatives to explore drone logistics capabilities. However, drones' limited delivery range due to their inherent battery capacity constraints remains a challenge. To address this issue, the hybrid delivery system, combining trucks and drones, has emerged as a promising solution and has been the focus of recent studies. Most prior research assumes that trucks park at customer locations while drones make auxiliary deliveries, but this approach can limit drone efficiency by restricting flights to customer sites, which may not always be the most suitable parking spots. A recent study proposed a new variation of the truck-drone delivery system, the Drone Routing Problem with Truck (DRP-T), which uses drones as the primary delivery system and features launch locations separate from customer sites (Jeong and Lee, 2021). DRP-T has demonstrated superior delivery capabilities, albeit with increased problem complexity. In this paper, we investigate the efficiency of establishing parking locations by comparing scenarios with customer location launching to further examine DRP-T's delivery capabilities. Additionally, we compare DRP-T to the well-known single truck, multi-drone model to further investigate DRP-T's potential. To mitigate DRP-T's complexity, we propose a computationally efficient heuristic algorithm. Numerical experiments quantitatively demonstrate the proposed algorithm's effectiveness and reveal substantial savings with DRP-T. Moreover, we present a sensitivity analysis to support economic decision-making in various operational environments." @default.
- W4367185959 created "2023-04-28" @default.
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- W4367185959 date "2023-10-01" @default.
- W4367185959 modified "2023-09-23" @default.
- W4367185959 title "Drone routing problem with truck: Optimization and quantitative analysis" @default.
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- W4367185959 doi "https://doi.org/10.1016/j.eswa.2023.120260" @default.
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