Abstract
The centralized seed packaging system faces significant challenges in dynamic decision-making due to stochastic demand fluctuations and operational constraints. This paper presents a novel hybrid approach integrating deep reinforcement learning (DRL) with multi-stage stochastic programming (MSP) to optimize decision-making processes. Our method leverages DRL for adaptive learning and MSP for uncertainty modeling, enabling real-time adjustments. Results from case studies demonstrate improved efficiency, reduced costs, and enhanced robustness compared to traditional approaches.
Keywords
Deep Reinforcement LearningMulti-Stage Stochastic ProgrammingReal-Time Decision-MakingSeed Packaging SystemsSupply Chain Optimization
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Cite This Article
Zhou, Y. (2024). Deep reinforcement learning-enhanced multi-stage stochastic programming for real-time decision-making in centralized seed packaging systems. Journal of Industrial Engineering and Applied Science, 2(6), 121-126. https://doi.org/10.70393/6a69656173.323435
Acknowledgments
The authors thank the editor and anonymous reviewers for their helpful comments and valuable suggestions.
Funding
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Data Availability Statement
The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author.
Informed Consent Statement
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Conflict of Interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Author Contributions
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References
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Copyright © 2024 The Author(s). Published by Southern United Academy of Sciences.
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