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Research on Optimization Strategies for Closed-Loop Supply Chain Management Based on Deep Learning Technology

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  • Chunjuan Gao

    (Nanjing Xiaozhuang University, China)

Abstract

This study explores the integration of deep learning (DL) technology and the guided simulated annealing algorithm (GSAA) to optimize closed-loop supply chains (CLSC) for sustainable development. By applying DL for predictive analysis and GSAA for optimization, the research aims to enhance CLSC operational efficiency and environmental sustainability. The methodology combines a review of the CLSC framework with practical applications of DL and GSAA, aiming to reduce waste, maximize resource utilization, and minimize environmental impact. An experimental comparison of this approach against traditional optimization strategies demonstrates the proposed method's superior effectiveness and efficiency. The findings reveal that the DL-GSAA optimization significantly improves CLSC sustainability and efficiency, with GSAA showing promising convergence properties. This study underscores the importance of advanced technological solutions in achieving sustainable supply chain management, offering practical insights for businesses and supply chain managers.

Suggested Citation

  • Chunjuan Gao, 2024. "Research on Optimization Strategies for Closed-Loop Supply Chain Management Based on Deep Learning Technology," International Journal of Information Systems and Supply Chain Management (IJISSCM), IGI Global, vol. 17(1), pages 1-22, January.
    • Handle: RePEc:igg:jisscm:v:17:y:2024:i:1:p:1-22
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    References listed on IDEAS

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    1. Wu, Xiaole & Zhou, Yu, 2019. "Buyer-specific versus uniform pricing in a closed-loop supply chain with third-party remanufacturing," European Journal of Operational Research, Elsevier, vol. 273(2), pages 548-560.
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