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

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This study optimizes e-commerce order batching to reduce picking time and costs. An improved algorithm balances multiple objectives, enhancing efficiency in distribution centers.

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Area of Science:

  • Operations Research
  • E-commerce Logistics
  • Supply Chain Management

Background:

  • E-commerce growth necessitates efficient warehousing operations.
  • Order batching is a key strategy for optimizing picking processes.
  • Current methods face challenges in balancing multiple, often conflicting, objectives.

Purpose of the Study:

  • To develop a model for order batching optimization in e-commerce warehousing.
  • To minimize order picking time, delay costs, and picking costs.
  • To achieve workload balance among picking staff.

Main Methods:

  • Established a multi-objective optimization model for order batching.
  • Designed an improved Non-dominated Sorting Genetic Algorithm II (NSGA-II).
  • Incorporated novel selection, crossover, and mutation strategies into the algorithm.

Main Results:

  • The proposed model and improved NSGA-II algorithm effectively balanced multiple optimization objectives.
  • Case studies validated the practical effectiveness of the developed approach.
  • Sensitivity analysis confirmed the robustness of the model parameters.

Conclusions:

  • The developed model and algorithm provide a significant theoretical and practical basis for enhancing order picking efficiency.
  • Optimized order batching leads to substantial improvements in e-commerce distribution center performance.
  • This research offers a viable solution for complex logistics challenges in online retail.