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Impact of drone delivery on sustainability and cost: Realizing the UAV potential through vehicle routing optimization

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  • Chiang, Wen-Chyuan
  • Li, Yuyu
  • Shang, Jennifer
  • Urban, Timothy L.

Abstract

An unmanned aerial vehicle (UAV), commonly known as a drone, offers the advantage of speed, flexibility, and ease in delivering goods to customers. They are particularly useful for tasks that are dull, hazardous, or dirty. Whether the use of drone delivery is beneficial to the environment and cost saving is still a topic under debate. Ideally, drones yield lower energy consumption and reduce greenhouse gas emissions, thus reducing the carbon footprint and enhancing environmental sustainability. In this research, we analytically study the impact of UAVs on CO2 emission and cost. We propose a mixed-integer (0–1 linear) green routing model for UAV to exploit the sustainability aspects of the use of UAVs for last-mile parcel deliveries. A genetic algorithm is developed to efficiently solve the complex model, and an extensive experiment is conducted to illustrate and validate the analytical model and the solution algorithm. We find that optimally routing and delivering packages with UAVs would save energy and reduce carbon emissions. The computational results strongly support the notion that using UAVs for last-mile logistics is not only cost effective, but also environmentally friendly.

Suggested Citation

  • Chiang, Wen-Chyuan & Li, Yuyu & Shang, Jennifer & Urban, Timothy L., 2019. "Impact of drone delivery on sustainability and cost: Realizing the UAV potential through vehicle routing optimization," Applied Energy, Elsevier, vol. 242(C), pages 1164-1175.
  • Handle: RePEc:eee:appene:v:242:y:2019:i:c:p:1164-1175
    DOI: 10.1016/j.apenergy.2019.03.117
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    References listed on IDEAS

    as
    1. G. Clarke & J. W. Wright, 1964. "Scheduling of Vehicles from a Central Depot to a Number of Delivery Points," Operations Research, INFORMS, vol. 12(4), pages 568-581, August.
    2. Gouveia, Luis, 1995. "A result on projection for the vehicle routing ptoblem," European Journal of Operational Research, Elsevier, vol. 85(3), pages 610-624, September.
    3. Leggieri, Valeria & Haouari, Mohamed, 2017. "A practical solution approach for the green vehicle routing problem," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 104(C), pages 97-112.
    4. G. B. Dantzig & J. H. Ramser, 1959. "The Truck Dispatching Problem," Management Science, INFORMS, vol. 6(1), pages 80-91, October.
    5. Bektas, Tolga & Laporte, Gilbert, 2011. "The Pollution-Routing Problem," Transportation Research Part B: Methodological, Elsevier, vol. 45(8), pages 1232-1250, September.
    6. Bielaczyc, Piotr & Woodburn, Joseph & Szczotka, Andrzej, 2014. "An assessment of regulated emissions and CO2 emissions from a European light-duty CNG-fueled vehicle in the context of Euro 6 emissions regulations," Applied Energy, Elsevier, vol. 117(C), pages 134-141.
    7. Belmonte, N. & Staulo, S. & Fiorot, S. & Luetto, C. & Rizzi, P. & Baricco, M., 2018. "Fuel cell powered octocopter for inspection of mobile cranes: Design, cost analysis and environmental impacts," Applied Energy, Elsevier, vol. 215(C), pages 556-565.
    8. Asadi, Ehsan & Habibi, Farhad & Nickel, Stefan & Sahebi, Hadi, 2018. "A bi-objective stochastic location-inventory-routing model for microalgae-based biofuel supply chain," Applied Energy, Elsevier, vol. 228(C), pages 2235-2261.
    9. Demir, Emrah & Bektaş, Tolga & Laporte, Gilbert, 2014. "The bi-objective Pollution-Routing Problem," European Journal of Operational Research, Elsevier, vol. 232(3), pages 464-478.
    10. Tiwari, Anurag & Chang, Pei-Chann, 2015. "A block recombination approach to solve green vehicle routing problem," International Journal of Production Economics, Elsevier, vol. 164(C), pages 379-387.
    11. Koç, Çağrı & Bektaş, Tolga & Jabali, Ola & Laporte, Gilbert, 2014. "The fleet size and mix pollution-routing problem," Transportation Research Part B: Methodological, Elsevier, vol. 70(C), pages 239-254.
    12. Yan Xia & Rajan Batta & Rakesh Nagi, 2017. "Controlling a Fleet of Unmanned Aerial Vehicles to Collect Uncertain Information in a Threat Environment," Operations Research, INFORMS, vol. 65(3), pages 674-692, June.
    13. Meng, Fanxin & Liu, Gengyuan & Yang, Zhifeng & Casazza, Marco & Cui, Shenghui & Ulgiati, Sergio, 2017. "Energy efficiency of urban transportation system in Xiamen, China. An integrated approach," Applied Energy, Elsevier, vol. 186(P2), pages 234-248.
    14. Gilbert Laporte, 2009. "Fifty Years of Vehicle Routing," Transportation Science, INFORMS, vol. 43(4), pages 408-416, November.
    15. Franceschetti, Anna & Honhon, Dorothée & Van Woensel, Tom & Bektaş, Tolga & Laporte, Gilbert, 2013. "The time-dependent pollution-routing problem," Transportation Research Part B: Methodological, Elsevier, vol. 56(C), pages 265-293.
    16. R. Baldacci & E. Hadjiconstantinou & A. Mingozzi, 2004. "An Exact Algorithm for the Capacitated Vehicle Routing Problem Based on a Two-Commodity Network Flow Formulation," Operations Research, INFORMS, vol. 52(5), pages 723-738, October.
    17. Yanjie Zhou & Gyu M. Lee, 2017. "A Lagrangian Relaxation-Based Solution Method for a Green Vehicle Routing Problem to Minimize Greenhouse Gas Emissions," Sustainability, MDPI, vol. 9(5), pages 1-17, May.
    18. Erdoğan, Sevgi & Miller-Hooks, Elise, 2012. "A Green Vehicle Routing Problem," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 48(1), pages 100-114.
    19. Turkensteen, Marcel, 2017. "The accuracy of carbon emission and fuel consumption computations in green vehicle routing," European Journal of Operational Research, Elsevier, vol. 262(2), pages 647-659.
    20. Qian, Jiani & Eglese, Richard, 2016. "Fuel emissions optimization in vehicle routing problems with time-varying speeds," European Journal of Operational Research, Elsevier, vol. 248(3), pages 840-848.
    21. Duan, Hongbo & Zhang, Gupeng & Wang, Shouyang & Fan, Ying, 2019. "Integrated benefit-cost analysis of China's optimal adaptation and targeted mitigation," Ecological Economics, Elsevier, vol. 160(C), pages 76-86.
    22. Xiao, Yiyong & Konak, Abdullah, 2016. "The heterogeneous green vehicle routing and scheduling problem with time-varying traffic congestion," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 88(C), pages 146-166.
    23. Niels Agatz & Paul Bouman & Marie Schmidt, 2018. "Optimization Approaches for the Traveling Salesman Problem with Drone," Transportation Science, INFORMS, vol. 52(4), pages 965-981, August.
    24. Zhang, Jianghua & Zhao, Yingxue & Xue, Weili & Li, Jin, 2015. "Vehicle routing problem with fuel consumption and carbon emission," International Journal of Production Economics, Elsevier, vol. 170(PA), pages 234-242.
    25. Donateo, Teresa & Ficarella, Antonio & Spedicato, Luigi & Arista, Alessandro & Ferraro, Marco, 2017. "A new approach to calculating endurance in electric flight and comparing fuel cells and batteries," Applied Energy, Elsevier, vol. 187(C), pages 807-819.
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