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Improved Compact Cuckoo Search Algorithm Applied to Location of Drone Logistics Hub

Author

Listed:
  • Jeng-Shyang Pan

    (College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao 266590, China)

  • Pei-Cheng Song

    (College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao 266590, China)

  • Shu-Chuan Chu

    (College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao 266590, China)

  • Yan-Jun Peng

    (College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao 266590, China)

Abstract

Drone logistics can play an important role in logistics at the end of the supply chain and special environmental logistics. At present, drone logistics is in the initial development stage, and the location of drone logistics hubs is an important issue in the optimization of logistics systems. This paper implements a compact cuckoo search algorithm with mixed uniform sampling technology, and, for the problem of weak search ability of the algorithm, this paper combines the method of recording the key positions of the search process and increasing the number of generated solutions to achieve further improvements, as well as implements the improved compact cuckoo search algorithm. Then, this paper uses 28 test functions to verify the algorithm. Aiming at the problem of the location of drone logistics hubs in remote areas or rural areas, this paper establishes a simple model that considers the traffic around the village, the size of the village, and other factors. It is suitable for selecting the location of the logistics hub in advance, reducing the cost of drone logistics, and accelerating the large-scale application of drone logistics. This paper uses the proposed algorithm for testing, and the test results indicate that the proposed algorithm has strong competitiveness in the proposed model.

Suggested Citation

  • Jeng-Shyang Pan & Pei-Cheng Song & Shu-Chuan Chu & Yan-Jun Peng, 2020. "Improved Compact Cuckoo Search Algorithm Applied to Location of Drone Logistics Hub," Mathematics, MDPI, vol. 8(3), pages 1-19, March.
    • Handle: RePEc:gam:jmathe:v:8:y:2020:i:3:p:333-:d:327741
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    References listed on IDEAS

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    1. Walton, S. & Hassan, O. & Morgan, K. & Brown, M.R., 2011. "Modified cuckoo search: A new gradient free optimisation algorithm," Chaos, Solitons & Fractals, Elsevier, vol. 44(9), pages 710-718.
    2. Ai-Qing Tian & Shu-Chuan Chu & Jeng-Shyang Pan & Huanqing Cui & Wei-Min Zheng, 2020. "A Compact Pigeon-Inspired Optimization for Maximum Short-Term Generation Mode in Cascade Hydroelectric Power Station," Sustainability, MDPI, vol. 12(3), pages 1-19, January.
    3. John Gunnar Carlsson & Siyuan Song, 2018. "Coordinated Logistics with a Truck and a Drone," Management Science, INFORMS, vol. 64(9), pages 4052-4069, September.
    4. Adrienne Welch Sudbury & E. Bruce Hutchinson, 2016. "A Cost Analysis of Amazon Prime Air (Drone Delivery)," Journal for Economic Educators, Middle Tennessee State University, Business and Economic Research Center, vol. 16(1), pages 1-12, Fall.
    5. 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.
    6. Jiyoon Park & Solhee Kim & Kyo Suh, 2018. "A Comparative Analysis of the Environmental Benefits of Drone-Based Delivery Services in Urban and Rural Areas," Sustainability, MDPI, vol. 10(3), pages 1-15, March.
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