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Multi-objective optimization of electronic product goods location assignment in stereoscopic warehouse based on adaptive genetic algorithm

Author

Listed:
  • Bo Yan

    (South China University of Technology)

  • Chang Yan

    (South China University of Technology)

  • Feng Long

    (South China University of Technology)

  • Xing-Chao Tan

    (South China University of Technology)

Abstract

Storage is an important part of commodity circulation. A certain amount of material must be stored to meet the needs of social production and consumption within a certain time to maintain the smooth process of social reproduction. This study focuses on warehousing optimization and goods location assignment when electronic products are stored in a stereoscopic storehouse. Moreover, this study is based on a theoretical study on genetic algorithm. On the basis of the background of the current warehouse management and cargo distribution of LCM module products warehouse belonging to W company, this study uses the dynamic goods location assignment strategy of stochastic inventory, and builds a multi-objective goods location assignment model of a stereoscopic warehouse. To simplify the calculations and improve the efficiency, we conduct a Matlab simulation on the basis of practical data by adopting a modified genetic operator and converting multi-objective optimization by the changing weight coefficient. The adaptive genetic algorithm can be used to make a multi-objective goods location assignment model that efficiently converges to the optimal solution.

Suggested Citation

  • Bo Yan & Chang Yan & Feng Long & Xing-Chao Tan, 2018. "Multi-objective optimization of electronic product goods location assignment in stereoscopic warehouse based on adaptive genetic algorithm," Journal of Intelligent Manufacturing, Springer, vol. 29(6), pages 1273-1285, August.
    • Handle: RePEc:spr:joinma:v:29:y:2018:i:6:d:10.1007_s10845-015-1177-7
    DOI: 10.1007/s10845-015-1177-7
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    References listed on IDEAS

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    Cited by:

    1. Lam, H.Y. & Ho, G.T.S. & Mo, Daniel Y. & Tang, Valerie, 2023. "Responsive pick face replenishment strategy for stock allocation to fulfil e-commerce order," International Journal of Production Economics, Elsevier, vol. 264(C).
    2. Jingran Liang & Zhengning Wu & Chenye Zhu & Zhi-Hai Zhang, 2022. "An estimation distribution algorithm for wave-picking warehouse management," Journal of Intelligent Manufacturing, Springer, vol. 33(4), pages 929-942, April.
    3. Huifang Li & Yin Wang, 2023. "Hierarchical Multimodal Hub Location Problem with Carbon Emissions," Sustainability, MDPI, vol. 15(3), pages 1-23, January.
    4. Zheng Wang & Wei Xu & Xiangpei Hu & Yong Wang, 2022. "Inventory allocation to robotic mobile-rack and picker-to-part warehouses at minimum order-splitting and replenishment costs," Annals of Operations Research, Springer, vol. 316(1), pages 467-491, September.
    5. Mengyue Zhai & Zheng Wang, 2024. "Optimizing Rack Locations in the Mobile-Rack Picking System: A Method of Integrating Rack Heat and Relevance," Mathematics, MDPI, vol. 12(3), pages 1-20, January.

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