Deep Reinforcement Learning-Enhanced Multi-Stage Stochastic Programming for Real-Time Decision-Making in Centralized Seed Packaging Systems

Yuqun Zhou

doi:10.70393/6a69656173.323435

OPEN ACCESS |Research Article ||1 December 2024

Deep Reinforcement Learning-Enhanced Multi-Stage Stochastic Programming for Real-Time Decision-Making in Centralized Seed Packaging Systems

Yuqun Zhou

University of Wisconsin-Madison

yzhou364@wisc.edu

University of Wisconsin-Madison, USA.

* Corresponding Author¹: Yuqun Zhou, E-Mail: yzhou364@wisc.edu

Publication

Accepted Unknow ; Published 2024 December 1

Journal of Industrial Engineering and Applied Science, 2024, 2(6), 3005-6071.

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 Learning , Multi-Stage Stochastic Programming , Real-Time Decision-Making , Seed Packaging Systems , Supply Chain Optimization .

Metadata

DOI:

10.70393/6a69656173.323435

ARK:

ark:/40704/JIEAS.v2n6a13

Pages: 121-126

References: 5

Disciplines: Artificial Intelligence Technology

Subjects: Machine Learning

Cite This Article

APA Style

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

Not applicable.

INSTITUTIONAL REVIEW BOARD STATEMENT

Not applicable.

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

Not applicable.

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

Not applicable.

References

1.

Chen, Y., Liu, Z., & Zhang, X. (2020). Deep reinforcement learning for dynamic decision-making in energy systems. IEEE Transactions on Energy Conversion, 35(4), 1234–1245.

2.

Kall, P., & Wallace, S. W. (1994). Stochastic Programming. Springer.

3.

Liu, R., Wang, F., & Zhang, T. (2021). A hybrid approach combining reinforcement learning and stochastic optimization for healthcare logistics. Operations Research for Health Care, 10(2), 102–115.

4.

Mnih, V., Kavukcuoglu, K., Silver, D., et al. (2015). Human-level control through deep reinforcement learning. Nature, 518(7540), 529–533.

5.

Schulman, J., Wolski, F., Dhariwal, P., et al. (2017). Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347.

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Copyright © 2025 The Author(s). Published by Southern United Academy of Sciences.
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