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Rural Logistics Distribution Center Location Selection Based on Improved Northern Goshawk Algorithm.

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Published:02 November 2023Publication History

ABSTRACT

In order to promote the strategy of rural revitalization, the central government strongly supports the project of "express delivery into villages". As a key part of the new logistics industry, the reasonable location of logistics distribution centers is important to improve the efficiency of rural logistics location. In view of the disadvantages of traditional algorithms for site selection optimization, such as low accuracy, easy to fall into local optimization, and the goal of reducing the rural distribution distance and the total cost of rural distribution centers, we propose an improved northern goshawk algorithm. The improved algorithm is based on a randomized inverse strategy to enhance the algorithm's optimization capability, and a sinusoidal chaos search to improve the population diversity and optimize the rural logistics distribution center location problem. Simulation experiments show that this improved northern goshawk algorithm can effectively avoid the local optimum phenomenon of the traditional algorithm compared with the traditional whale optimization algorithm and sparrow search algorithm, and reduce the rural logistics distribution distance, total cost of distribution center and algorithm running time.

References

  1. John Wilson. Optimal Scheme Design of Ship Logistics Distribution Center Location Based on Ant Colony Algorithm[J]. Computer Informatization and Mechanical System,2022,5(3).Google ScholarGoogle Scholar
  2. Thomas Michely. Location planning of ship logistics distribution center considering time window[J]. Computer Informatization and Mechanical System,2022,5(2).Google ScholarGoogle Scholar
  3. Khairunissa Maryam,Lee Hyunsoo. Hybrid Metaheuristic-Based Spatial Modeling and Analysis of Logistics Distribution Center[J]. ISPRS International Journal of Geo-Information,2021,11(1).Google ScholarGoogle Scholar
  4. Shang M, Kang JY, Cao JW, Wan ZP. Logistics distribution center site selection strategy based on improved whale optimization algorithm[J]. Computer Application and Software,2019,36(06):254-259.Google ScholarGoogle Scholar
  5. Cui Huixia,Chen Xiangyong,Guo Ming,Jiao Yang,Cao Jinde,Qiu Jianlong. A distribution center location optimization model based on minimizing operating costs under uncertain demand with logistics node capacity scalability[J]. Physica A: Statistical Mechanics and its Applications,2023,610.Google ScholarGoogle Scholar
  6. Mei Zhimin,Chi Xuexin,Chi Rui. Research on Logistics Distribution Center Location Based on Hybrid Beetle Antennae Search and Rain Algorithm.[J]. Biomimetics (Basel, Switzerland),2022,7(4).Google ScholarGoogle Scholar
  7. Zhu Anqing,Wen Youyun. Green Logistics Location-Routing Optimization Solution Based on Improved GA A1gorithm considering Low-Carbon and Environmental Protection[J]. Journal of Mathematics,2021,2021.Google ScholarGoogle Scholar
  8. Cheng Yusi,Pan Xinwei. Design of a Support System for Complicated Logistics Location Integrating Big Data[J]. Advances in Civil Engineering,2021,2021.Google ScholarGoogle Scholar
  9. Yan Sun,Yue Lu,Cevin Zhang. Fuzzy Linear Programming Models for a Green Logistics Center Location and Allocation Problem under Mixed Uncertainties Based on Different Carbon Dioxide Emission Reduction Methods[J]. Sustainability,2019,11(22).Google ScholarGoogle Scholar
  10. Gaoyan Lyu,Lihua Chen,Baofeng Huo. The impact of logistics platforms and location on logistics resource integration and operational performance[J]. The International Journal of Logistics Management,2019,30(2).Google ScholarGoogle Scholar
  11. Juan Li,Dan-dan Xiao,Hong Lei,Ting Zhang,Tian Tian. Using Cuckoo Search Algorithm with Q-Learning and Genetic Operation to Solve the Problem of Logistics Distribution Center Location[J]. Mathematics,2020,8(2).Google ScholarGoogle Scholar
  12. Mihrimah Özmen,Emel Kızılkaya Aydoğan. Robust multi-criteria decision making methodology for real life logistics center location problem[J]. Artificial Intelligence Review: An International Science and Engineering Journal,2020,53(12).Google ScholarGoogle Scholar
  13. Dehghani M, Hubálovský Š, Trojovský P. Northern goshawk optimization: a new swarm-based algorithm for solving optimization problems[J]. IEEE Access, 2021, 9: 162059-162080.Google ScholarGoogle ScholarCross RefCross Ref
  14. Lijuan Y ,Xiedong S . High-Performance Computing Analysis and Location Selection of Logistics Distribution Center Space Based on Whale Optimization Algorithm[J]. Computational Intelligence and Neuroscience,2022,2022.Google ScholarGoogle Scholar
  15. Ruan Xinbo,Liu Lihua,Chen Lijin. Application of sparrow search algorithm in logistics distribution center site selection[J]. Logistics Technology,2021,40(12):40-43+101.Google ScholarGoogle Scholar
  16. Wang Kun. Simulation study of ant colony algorithm logistics distribution center site selection optimization[J]. Computer Simulation, 2012(4):251-254.Google ScholarGoogle Scholar
  17. Lin TsungXian,Wu ZhongHuan,Pan WenTsao. Optimal location of logistics distribution centres with swarm intelligent clustering algorithms.[J]. PloS one,2022,17(8).Google ScholarGoogle Scholar

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  1. Rural Logistics Distribution Center Location Selection Based on Improved Northern Goshawk Algorithm.

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    • Published in

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      BDIOT '23: Proceedings of the 2023 7th International Conference on Big Data and Internet of Things
      August 2023
      232 pages
      ISBN:9798400708015
      DOI:10.1145/3617695

      Copyright © 2023 ACM

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      Publication History

      • Published: 2 November 2023

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