IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v16y2024i9p3612-d1383014.html
   My bibliography  Save this article

Autonomous Agent-Based Adaptation of Energy-Optimized Production Schedules Using Extensive-Form Games

Author

Listed:
  • William Motsch

    (Technologie-Initiative SmartFactory KL e.V., Trippstadter Str. 122, 67663 Kaiserslautern, Germany)

  • Achim Wagner

    (German Research Center for Artificial Intelligence (DFKI), Trippstadter Str. 122, 67663 Kaiserslautern, Germany)

  • Martin Ruskowski

    (German Research Center for Artificial Intelligence (DFKI), Trippstadter Str. 122, 67663 Kaiserslautern, Germany)

Abstract

Modular cyber-physical production systems are an important paradigm of Industry 4.0 to react flexibly to changes. The flexibility of those systems is further increased with skill-based engineering and can be used to adapt to customer requirements or to adapt manufacturing to disturbances in supply chains. Further potential for application of these systems can be found in the topic of electrical energy supply, which is also characterized by fluctuations. The relevance of energy-optimized production schedules for manufacturing systems in general becomes more important with the increased use of renewable energies. Nevertheless, it is often difficult to adapt when short-term energy price updates or unforeseen events occur. To address these challenges with an autonomous approach, this contribution focuses on extensive-form games to adapt energy-optimized production schedules in an agent-based manner. The paper presents agent-based modeling to transform and monitor energy-optimized production schedules into game trees to respond to changing energy prices and disturbances in production. The game is setup with a scheduler agent and energy agents who are considered players. The implementation of the mechanism is presented in two use cases, realizing decision making for an energy price update in a simulation example and for unforeseen events in a real-world demonstrator.

Suggested Citation

  • William Motsch & Achim Wagner & Martin Ruskowski, 2024. "Autonomous Agent-Based Adaptation of Energy-Optimized Production Schedules Using Extensive-Form Games," Sustainability, MDPI, vol. 16(9), pages 1-30, April.
  • Handle: RePEc:gam:jsusta:v:16:y:2024:i:9:p:3612-:d:1383014
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/16/9/3612/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2071-1050/16/9/3612/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Silviu Raileanu & Florin Anton & Alexandru Iatan & Theodor Borangiu & Silvia Anton & Octavian Morariu, 2017. "Resource scheduling based on energy consumption for sustainable manufacturing," Journal of Intelligent Manufacturing, Springer, vol. 28(7), pages 1519-1530, October.
    2. Finn, Paddy & Fitzpatrick, Colin, 2014. "Demand side management of industrial electricity consumption: Promoting the use of renewable energy through real-time pricing," Applied Energy, Elsevier, vol. 113(C), pages 11-21.
    3. Hart, Sergiu, 1992. "Games in extensive and strategic forms," Handbook of Game Theory with Economic Applications, in: R.J. Aumann & S. Hart (ed.), Handbook of Game Theory with Economic Applications, edition 1, volume 1, chapter 2, pages 19-40, Elsevier.
    4. Gahm, Christian & Denz, Florian & Dirr, Martin & Tuma, Axel, 2016. "Energy-efficient scheduling in manufacturing companies: A review and research framework," European Journal of Operational Research, Elsevier, vol. 248(3), pages 744-757.
    5. M. Saqlain & S. Ali & J. Y. Lee, 2023. "A Monte-Carlo tree search algorithm for the flexible job-shop scheduling in manufacturing systems," Flexible Services and Manufacturing Journal, Springer, vol. 35(2), pages 548-571, June.
    Full references (including those not matched with items on IDEAS)

    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. Golpîra, Hêriş, 2020. "Smart Energy-Aware Manufacturing Plant Scheduling under Uncertainty: A Risk-Based Multi-Objective Robust Optimization Approach," Energy, Elsevier, vol. 209(C).
    2. Förster, Robert & Harding, Sebastian & Buhl, Hans Ulrich, 2024. "Unleashing the economic and ecological potential of energy flexibility: Attractiveness of real-time electricity tariffs in energy crises," Energy Policy, Elsevier, vol. 185(C).
    3. João M. R. C. Fernandes & Seyed Mahdi Homayouni & Dalila B. M. M. Fontes, 2022. "Energy-Efficient Scheduling in Job Shop Manufacturing Systems: A Literature Review," Sustainability, MDPI, vol. 14(10), pages 1-34, May.
    4. Abbas Hamze & Yassine Ouazene & Nazir Chebbo & Imane Maatouk, 2019. "Multisources of Energy Contracting Strategy with an Ecofriendly Factor and Demand Uncertainties," Energies, MDPI, vol. 12(20), pages 1-24, October.
    5. Xiaoyan Li & Xuedong Liang & Zhi Li, 2023. "The Strategy of Strengthening Efficiency and Environmental Performance of Product Changeover in the Multiproduct Production System," SAGE Open, , vol. 13(3), pages 21582440231, September.
    6. Zheng, Yingying & Jenkins, Bryan M. & Kornbluth, Kurt & Kendall, Alissa & Træholt, Chresten, 2018. "Optimization of a biomass-integrated renewable energy microgrid with demand side management under uncertainty," Applied Energy, Elsevier, vol. 230(C), pages 836-844.
    7. Hajo Terbrack & Thorsten Claus & Frank Herrmann, 2021. "Energy-Oriented Production Planning in Industry: A Systematic Literature Review and Classification Scheme," Sustainability, MDPI, vol. 13(23), pages 1-32, December.
    8. Behnam Zakeri & Samuli Rinne & Sanna Syri, 2015. "Wind Integration into Energy Systems with a High Share of Nuclear Power—What Are the Compromises?," Energies, MDPI, vol. 8(4), pages 1-35, March.
    9. Fernando, Yudi & Hor, Wei Lin, 2017. "Impacts of energy management practices on energy efficiency and carbon emissions reduction: A survey of malaysian manufacturing firms," Resources, Conservation & Recycling, Elsevier, vol. 126(C), pages 62-73.
    10. Catanzaro, Daniele & Pesenti, Raffaele & Ronco, Roberto, 2023. "Job scheduling under Time-of-Use energy tariffs for sustainable manufacturing: a survey," European Journal of Operational Research, Elsevier, vol. 308(3), pages 1091-1109.
    11. Jin, Hongyang & Li, Zhengshuo & Sun, Hongbin & Guo, Qinglai & Chen, Runze & Wang, Bin, 2017. "A robust aggregate model and the two-stage solution method to incorporate energy intensive enterprises in power system unit commitment," Applied Energy, Elsevier, vol. 206(C), pages 1364-1378.
    12. Jiang, Sheng-Long & Wang, Meihong & Bogle, I. David L., 2023. "Plant-wide byproduct gas distribution under uncertainty in iron and steel industry via quantile forecasting and robust optimization," Applied Energy, Elsevier, vol. 350(C).
    13. Ghorbanzadeh, Masoumeh & Ranjbar, Mohammad, 2023. "Energy-aware production scheduling in the flow shop environment under sequence-dependent setup times, group scheduling and renewable energy constraints," European Journal of Operational Research, Elsevier, vol. 307(2), pages 519-537.
    14. Murphy, M.D. & O’Mahony, M.J. & Upton, J., 2015. "Comparison of control systems for the optimisation of ice storage in a dynamic real time electricity pricing environment," Applied Energy, Elsevier, vol. 149(C), pages 392-403.
    15. Zheng, Menglian & Meinrenken, Christoph J. & Lackner, Klaus S., 2014. "Agent-based model for electricity consumption and storage to evaluate economic viability of tariff arbitrage for residential sector demand response," Applied Energy, Elsevier, vol. 126(C), pages 297-306.
    16. Huber, Matthias & Dimkova, Desislava & Hamacher, Thomas, 2014. "Integration of wind and solar power in Europe: Assessment of flexibility requirements," Energy, Elsevier, vol. 69(C), pages 236-246.
    17. Prakash Shenoy, 1998. "Game Trees For Decision Analysis," Theory and Decision, Springer, vol. 44(2), pages 149-171, April.
    18. Jiang, Sheng-Long & Peng, Gongzhuang & Bogle, I. David L. & Zheng, Zhong, 2022. "Two-stage robust optimization approach for flexible oxygen distribution under uncertainty in integrated iron and steel plants," Applied Energy, Elsevier, vol. 306(PB).
    19. Sivaneasan, Balakrishnan & Kandasamy, Nandha Kumar & Lim, May Lin & Goh, Kwang Ping, 2018. "A new demand response algorithm for solar PV intermittency management," Applied Energy, Elsevier, vol. 218(C), pages 36-45.
    20. Sven Schulz & Udo Buscher & Liji Shen, 2020. "Multi-objective hybrid flow shop scheduling with variable discrete production speed levels and time-of-use energy prices," Journal of Business Economics, Springer, vol. 90(9), pages 1315-1343, November.

    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:gam:jsusta:v:16:y:2024:i:9:p:3612-:d:1383014. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.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.