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Research on the application of deep learning in low-carbon supply chain management

Author

Listed:
  • Tian Zhao
  • Junting Lou

Abstract

This study proposes a deep learning-based framework to improve the efficiency and sustainability of LCSCM. Firstly, a multi-scale time series decomposition LSTM (MS-TDLSTM) model is proposed, which combines empirical mode decomposition (EMD) and attention mechanism to capture multi-scale characteristics of carbon emission data. Secondly, a multi-objective optimization model based on deep reinforcement learning (DRL) is designed. Through soft constraint multi-objective reinforcement learning, the prediction and optimization processes are integrated into a unified system, and intelligent decision-making of LCSCM is realized.

Suggested Citation

  • Tian Zhao & Junting Lou, 2025. "Research on the application of deep learning in low-carbon supply chain management," International Journal of Low-Carbon Technologies, Oxford University Press, vol. 20, pages 209-216.
    • Handle: RePEc:oup:ijlctc:v:20:y:2025:i::p:209-216.
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    File URL: http://hdl.handle.net/10.1093/ijlct/ctae290
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