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Research on order batching optimization based on improved NSGA-II algorithm

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  • Huiyue Xu
  • Juping Shao
  • Yanan Sun

Abstract

In the context of e-commerce, the order batching optimization problem in e-commerce warehousing centers has been addressed by establishing a model aimed at minimizing the order picking time, order delay costs, and picking costs, as well as achieving workload balance. An improved NSGA-II algorithm has been designed, which enhances the search capability and solution diversity by introducing new selection mechanisms and crossover mutation strategies. This approach more effectively balances multiple optimization objectives and validates the effectiveness of the model and algorithm with case studies, while also conducting sensitivity analysis on model parameters. The research results indicate that the established model and the designed algorithm are effective, providing a theoretical basis and practical significance for the optimization of order picking efficiency in e-commerce distribution centers.

Suggested Citation

  • Huiyue Xu & Juping Shao & Yanan Sun, 2025. "Research on order batching optimization based on improved NSGA-II algorithm," PLOS ONE, Public Library of Science, vol. 20(2), pages 1-29, February.
    • Handle: RePEc:plo:pone00:0319182
    DOI: 10.1371/journal.pone.0319182
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    References listed on IDEAS

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    1. Ardjmand, Ehsan & Shakeri, Heman & Singh, Manjeet & Sanei Bajgiran, Omid, 2018. "Minimizing order picking makespan with multiple pickers in a wave picking warehouse," International Journal of Production Economics, Elsevier, vol. 206(C), pages 169-183.
    2. Chen, Tzu-Li & Cheng, Chen-Yang & Chen, Yin-Yann & Chan, Li-Kai, 2015. "An efficient hybrid algorithm for integrated order batching, sequencing and routing problem," International Journal of Production Economics, Elsevier, vol. 159(C), pages 158-167.
    3. Jianbin Li & Rihuan Huang & James B. Dai, 2017. "Joint optimisation of order batching and picker routing in the online retailer’s warehouse in China," International Journal of Production Research, Taylor & Francis Journals, vol. 55(2), pages 447-461, January.
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