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MADRL-Based Intelligent Resource Allocation for Green Sports Events

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  • Jinliang Luo

    (Shandong Jianzhu University, China)

Abstract

Sustainability in sports events demands highly efficient resource management. This paper introduces an intelligent decision support system utilizing multi-agent deep reinforcement learning to optimize resource allocation in green sports events. The system enhances human resource scheduling and network resource management, effectively addressing dynamic conditions and resource imbalance issues. Correlation analysis validates the societal sustainability value of green sports initiatives. Comparative studies show that multi-agent deep reinforcement learning outperforms conventional algorithms in processing speed and feedback efficiency, providing balanced resource distribution with minimal network overhead. Experimental results demonstrate significant improvements in operational efficiency and responsiveness to audience flow changes. These findings provide valuable insights for sustainable event management.

Suggested Citation

  • Jinliang Luo, 2025. "MADRL-Based Intelligent Resource Allocation for Green Sports Events," International Journal of Decision Support System Technology (IJDSST), IGI Global Scientific Publishing, vol. 17(1), pages 1-23, January.
    • Handle: RePEc:igg:jdsst0:v:17:y:2025:i:1:p:1-23
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