IDEAS home Printed from https://ideas.repec.org/a/spr/operea/v22y2022i5d10.1007_s12351-022-00723-z.html
   My bibliography  Save this article

Optimal policy computing for blockchain based smart contracts via federated learning

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s12351-022-00723-z
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s12351-022-00723-z?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    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. 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.
    3. repec:taf:nmcmxx:v:24:y:2018:i:5:p:506-552 is not listed on IDEAS
    4. 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.
    5. 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.
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Sato, Kimitoshi, 2019. "Price Trends and Dynamic Pricing in Perishable Product Market Consisting of Superior and Inferior Firms," European Journal of Operational Research, Elsevier, vol. 274(1), pages 214-226.
    2. Kimitoshi Sato, 2021. "Dynamic pricing with automated purchase-reservation algorithms," Journal of Revenue and Pricing Management, Palgrave Macmillan, vol. 20(1), pages 33-41, February.
    3. Yuri Levin & Mikhail Nediak & Andrei Bazhanov, 2014. "Quantity Premiums and Discounts in Dynamic Pricing," Operations Research, INFORMS, vol. 62(4), pages 846-863, August.
    4. Yusen Xia & Jian Yang & Tingting Zhou, 2019. "Revenue management under randomly evolving economic conditions," Naval Research Logistics (NRL), John Wiley & Sons, vol. 66(1), pages 73-89, February.
    5. Chatwin, Richard E., 2000. "Optimal dynamic pricing of perishable products with stochastic demand and a finite set of prices," European Journal of Operational Research, Elsevier, vol. 125(1), pages 149-174, August.
    6. Schulte, Benedikt & Sachs, Anna-Lena, 2020. "The price-setting newsvendor with Poisson demand," European Journal of Operational Research, Elsevier, vol. 283(1), pages 125-137.
    7. Ali Hortaçsu & Olivia R. Natan & Hayden Parsley & Timothy Schwieg & Kevin R. Williams, 2021. "Organizational Structure and Pricing: Evidence from a Large U.S. Airline," NBER Working Papers 29508, National Bureau of Economic Research, Inc.
    8. Dasci, A. & Karakul, M., 2009. "Two-period dynamic versus fixed-ratio pricing in a capacity constrained duopoly," European Journal of Operational Research, Elsevier, vol. 197(3), pages 945-968, September.
    9. Jun Li & Serguei Netessine & Sergei Koulayev, 2018. "Price to Compete … with Many: How to Identify Price Competition in High-Dimensional Space," Management Science, INFORMS, vol. 64(9), pages 4118-4136, September.
    10. Escobari, Diego, 2014. "Estimating dynamic demand for airlines," Economics Letters, Elsevier, vol. 124(1), pages 26-29.
    11. Yiwei Chen & Vivek F. Farias, 2013. "Simple Policies for Dynamic Pricing with Imperfect Forecasts," Operations Research, INFORMS, vol. 61(3), pages 612-624, June.
    12. Ibrahim Mohammed & Basak Denizci Guillet & Rob Law & Wassiuw Abdul Rahaman, 2021. "Predicting the direction of dynamic price adjustment in the Hong Kong hotel industry," Tourism Economics, , vol. 27(2), pages 346-364, March.
    13. Xiao, Baichun & Yang, Wei, 2021. "A Bayesian learning model for estimating unknown demand parameter in revenue management," European Journal of Operational Research, Elsevier, vol. 293(1), pages 248-262.
    14. Aniruddha Dutta, 2019. "Capacity Allocation of Game Tickets Using Dynamic Pricing," Data, MDPI, vol. 4(4), pages 1-12, October.
    15. Namin, Aidin & Soysal, Gonca P. & Ratchford, Brian T., 2022. "Alleviating demand uncertainty for seasonal goods: An analysis of attribute-based markdown policy for fashion retailers," Journal of Business Research, Elsevier, vol. 145(C), pages 671-681.
    16. Yiwei Chen & Nikolaos Trichakis, 2021. "Technical Note—On Revenue Management with Strategic Customers Choosing When and What to Buy," Operations Research, INFORMS, vol. 69(1), pages 175-187, January.
    17. Yuri Levin & Jeff McGill & Mikhail Nediak, 2008. "Risk in Revenue Management and Dynamic Pricing," Operations Research, INFORMS, vol. 56(2), pages 326-343, April.
    18. Davide Crapis & Bar Ifrach & Costis Maglaras & Marco Scarsini, 2017. "Monopoly Pricing in the Presence of Social Learning," Management Science, INFORMS, vol. 63(11), pages 3586-3608, November.
    19. Qi Chen & Qi Xu & Wenjie Wang, 2019. "Optimal Policies for the Pricing and Replenishment of Fashion Apparel considering the Effect of Fashion Level," Complexity, Hindawi, vol. 2019, pages 1-12, February.
    20. Adam J. Mersereau & Dan Zhang, 2012. "Markdown Pricing with Unknown Fraction of Strategic Customers," Manufacturing & Service Operations Management, INFORMS, vol. 14(3), pages 355-370, July.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:operea:v:22:y:2022:i:5:d:10.1007_s12351-022-00723-z. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.