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VRP of Drones Considering Power Consumption Rate and Wind Effects

Author

Listed:
  • Kim Seongheon

    (Republic of Korea Air Force, Air Combat Command, Daegu, Republic of Korea)

  • Kim Suhwan

    (Korea National Defense University, Department of Military Science, Nonsan, Republic of Korea)

Abstract

The drone industry is one of the most important areas of the Fourth Industrial Revolution. In the drone industry, delivery systems using drones are now facing commercialization as they have undergone many experiments and discussions. The purpose of this study is to find the best route in a delivery system using a drone. In this study, we have developed the existing Vehicle Routing Problem (VRP) into a more realistic mathematical model considering the two differences between drones and vehicles; one is that power consumption varies with the weight of the loaded cargo and the other is that velocity is influenced by wind. This study also presents an Ant Colony System (ACS) algorithm to effectively solve VRP, a well-known NP-hard problem. The methodology of this study is quite successful and is expected to enable more realistic and effective routing decisions.

Suggested Citation

  • Kim Seongheon & Kim Suhwan, 2022. "VRP of Drones Considering Power Consumption Rate and Wind Effects," LOGI – Scientific Journal on Transport and Logistics, Sciendo, vol. 13(1), pages 210-221, January.
  • Handle: RePEc:vrs:logitl:v:13:y:2022:i:1:p:210-221:n:15
    DOI: 10.2478/logi-2022-0019
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    References listed on IDEAS

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    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. G. B. Dantzig & J. H. Ramser, 1959. "The Truck Dispatching Problem," Management Science, INFORMS, vol. 6(1), pages 80-91, October.
    3. 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.
    4. Myeong-hwan Hwang & Hyun-Rok Cha & Sung Yong Jung, 2018. "Practical Endurance Estimation for Minimizing Energy Consumption of Multirotor Unmanned Aerial Vehicles," Energies, MDPI, vol. 11(9), pages 1-11, August.
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