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A Multi-Objective Method Based on Tag Eigenvalues Is Used to Predict the Supply Chain for Online Retailers

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Listed:
  • Leilei Jiang

    (Anhui Open University, China)

  • Pan Hu

    (Anhui Open University, China)

  • Ke Dong

    (Anhui Open University, China)

  • Lu Wang

    (Anhui Open University, China)

Abstract

E-commerce has grown quickly in recent years thanks to advancements in Internet and information technologies. For the majority of consumers, online shopping has emerged as a primary mode of shopping. However, it has become more challenging for businesses to satisfy consumer demand due to their increasingly individualized wants. To address the need for customized products with numerous kinds and small quantities, businesses must rebuild their supply chain systems to increase their efficiency and adaptability. The SI-LSF technique, which employs boosting learning in the target-relative feature space to lower the prediction error and enhance the algorithm's capacity to handle input-output interactions, is validated in this study using a genuine industrial dataset. The study successfully identifies the relationship between sales and sales as well as target-specific features by applying the multi-objective regression integration algorithm based on label-specific features to a real-world supply chain demand scenario.

Suggested Citation

  • Leilei Jiang & Pan Hu & Ke Dong & Lu Wang, 2024. "A Multi-Objective Method Based on Tag Eigenvalues Is Used to Predict the Supply Chain for Online Retailers," International Journal of Information Systems and Supply Chain Management (IJISSCM), IGI Global, vol. 17(1), pages 1-15, January.
    • Handle: RePEc:igg:jisscm:v:17:y:2024:i:1:p:1-15
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    References listed on IDEAS

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    1. Wu, Chunying & Wang, Jianzhou & Hao, Yan, 2022. "Deterministic and uncertainty crude oil price forecasting based on outlier detection and modified multi-objective optimization algorithm," Resources Policy, Elsevier, vol. 77(C).
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