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Multi-Objective Optimization Information Fusion and Its Applications for Logistics Centers Maximum Coverage

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  • Xiao Ya Deng

    (School of Intelligent Manufacturing, Sichuan University of Arts and Science, China)

Abstract

From past the development direction of logistics centers covering problem, the main solution is almost always relying on modern computer and gradually developed intelligent algorithm, at the same time, the previous understanding of dynamic covering location model is not "dynamic", in order to improve the unreasonable distribution of logistics centers deployment time, improve the service coverage, coverage as the optimization goal to logistics centers, logistics centers as well as each one can be free to move according to certain rules of "dot", according to the conditions set by the site moved to a more reasonable. The innovation of all algorithms in this paper lies in that the logistics centers themselves are regarded as the subject of free "activities", and they are allowed to move freely according to these rules by setting certain moving rules. Simulation results show that the algorithm has good coverage effect and can meet the requirements of logistics centers for coverage effect.

Suggested Citation

  • Xiao Ya Deng, 2022. "Multi-Objective Optimization Information Fusion and Its Applications for Logistics Centers Maximum Coverage," International Journal of Information Systems and Supply Chain Management (IJISSCM), IGI Global, vol. 15(2), pages 1-12, April.
    • Handle: RePEc:igg:jisscm:v:15:y:2022:i:2:p:1-12
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    References listed on IDEAS

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    1. Schmid, Fabian & Winzer, Joscha & Pasemann, André & Behrendt, Frank, 2021. "An open-source modeling tool for multi-objective optimization of renewable nano/micro-off-grid power supply system: Influence of temporal resolution, simulation period, and location," Energy, Elsevier, vol. 219(C).
    2. Attia, Ahmed M. & Al Hanbali, Ahmad & Saleh, Haitham H. & Alsawafy, Omar G. & Ghaithan, Ahmed M. & Mohammed, Awsan, 2021. "A multi-objective optimization model for sizing decisions of a grid-connected photovoltaic system," Energy, Elsevier, vol. 229(C).
    3. Ma, Xuemin & Yang, Jingming & Sun, Hao & Hu, Ziyu & Wei, Lixin, 2021. "Feature information prediction algorithm for dynamic multi-objective optimization problems," European Journal of Operational Research, Elsevier, vol. 295(3), pages 965-981.
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