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Optimal policy computing for blockchain based smart contracts via federated learning

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  • Wanyang Dai

    (Nanjing University)

Abstract

In this paper, we develop a blockchain based decision-making system via federated learning along with an evolving convolution neural net, which can be applied to assemble-to-order services and Metaverses. The design and analysis of an optimal policy computing algorithm for smart contracts within the blockchain will be the focus. Inside the system, each order associated with a demand may simultaneously require multiple service items from different suppliers and the corresponding arrival rate may depend on blockchain history data represented by a long-range dependent stochastic process. The optimality of the computed dynamic policy on maximizing the expected infinite-horizon discounted profit is proved concerning both demand and supply rate controls with dynamic pricing and sequential packaging scheduling in an integrated fashion. Our policy is a pathwise oriented one and can be easily implemented online. The effectiveness of our optimal policy is supported by simulation comparisons.

Suggested Citation

  • Wanyang Dai, 2022. "Optimal policy computing for blockchain based smart contracts via federated learning," Operational Research, Springer, vol. 22(5), pages 5817-5844, November.
    • Handle: RePEc:spr:operea:v:22:y:2022:i:5:d:10.1007_s12351-022-00723-z
    DOI: 10.1007/s12351-022-00723-z
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    References listed on IDEAS

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    1. Guillermo Gallego & Garrett van Ryzin, 1994. "Optimal Dynamic Pricing of Inventories with Stochastic Demand over Finite Horizons," Management Science, INFORMS, vol. 40(8), pages 999-1020, August.
    2. Wanyang Dai, 2013. "Optimal Rate Scheduling via Utility-Maximization for J -User MIMO Markov Fading Wireless Channels with Cooperation," Operations Research, INFORMS, vol. 61(6), pages 1450-1462, December.
    3. Wanyang Dai, 2018. "Platform modelling and scheduling game with multiple intelligent cloud-computing pools for big data," Mathematical and Computer Modelling of Dynamical Systems, Taylor & Francis Journals, vol. 24(5), pages 526-572, September.
    4. Xiaowei Xu & Wallace J. Hopp, 2009. "Technical Note---Price Trends in a Dynamic Pricing Model with Heterogeneous Customers: A Martingale Perspective," Operations Research, INFORMS, vol. 57(5), pages 1298-1302, October.
    5. repec:taf:nmcmxx:v:24:y:2018:i:5:p:506-552 is not listed on IDEAS
    6. Wanyang Dai, 2019. "Quantum-computing with AI & blockchain: modelling, fault tolerance and capacity scheduling," Mathematical and Computer Modelling of Dynamical Systems, Taylor & Francis Journals, vol. 25(6), pages 523-559, November.
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    Cited by:

    1. Huizhu Ju & Qingcheng Zeng & Xiang Chu & Yimeng Li, 2024. "Cooperative investment strategies of ports and shipping companies in blockchain technology," Operational Research, Springer, vol. 24(2), pages 1-33, June.

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