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Multi-agent task planning and resource apportionment in a smart grid

Author

Listed:
  • Min Chen

    (Hohhot Vocational College)

  • Ashutosh Sharma

    (Southern Federal University)

  • Jyoti Bhola

    (National Institute of Technology)

  • Tien V. T. Nguyen

    (Industrial University of Ho Chi Minh City)

  • Chinh V. Truong

    (Industrial University of Ho Chi Minh City)

Abstract

Nowadays, in different fields, tremendous attention is received by the Multi-agent systems for complex problem solutions with smaller task subdivision. Multiple inputs are utilized, e.g., history of actions, interactions with its neighboring agents by an agent. By the existing techniques for the task planning of the control structure the low efficiency is exhibited. By utilizing the sole numerical analysis method for a complicated distributed resource planning problem, the satisfactory optimal solution is impossible to obtain. In this paper, the control structure model is presented based on the multi-agents, in which the multi-agents superiority is exploited for complex task achievement. The collaboration of multi-agent framework is redefined, and the local conflict coordination mechanism is developed. Moreover, the high adaptability and superior cooperation are exhibited by the presented technique. The function value and its time–space complexity are analyzed, and it is obtained that the lower objective function value is achieved by the algorithm and the better convergence and adaptability are exhibited. The presented technique is 37–43% better than the Hierarchical Task Network Planning (HTN) technique for different time slots. The performance of the presented technique is 29–34% better compared to the Time Preference HTN technique in terms of function value. The performance of the proposed technique is better compared to the existing techniques in terms of obtained function values.

Suggested Citation

  • Min Chen & Ashutosh Sharma & Jyoti Bhola & Tien V. T. Nguyen & Chinh V. Truong, 2022. "Multi-agent task planning and resource apportionment in a smart grid," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 13(1), pages 444-455, March.
    • Handle: RePEc:spr:ijsaem:v:13:y:2022:i:1:d:10.1007_s13198-021-01467-3
    DOI: 10.1007/s13198-021-01467-3
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    References listed on IDEAS

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    1. Sergey Grachev & Petr Skobelev & Igor Mayorov & Elena Simonova, 2020. "Adaptive Clustering through Multi-Agent Technology: Development and Perspectives," Mathematics, MDPI, vol. 8(10), pages 1-17, September.
    2. Alfonso González-Briones & Fernando De La Prieta & Mohd Saberi Mohamad & Sigeru Omatu & Juan M. Corchado, 2018. "Multi-Agent Systems Applications in Energy Optimization Problems: A State-of-the-Art Review," Energies, MDPI, vol. 11(8), pages 1-28, July.
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    Cited by:

    1. Nan Zhao & Chun Feng, 2023. "Research on Multi-AGV Task Allocation in Train Unit Maintenance Workshop," Mathematics, MDPI, vol. 11(16), pages 1-18, August.
    2. Huang Huang & Xinwei Cuan & Zhuo Chen & Lina Zhang & Hao Chen, 2023. "A Multiregional Agricultural Machinery Scheduling Method Based on Hybrid Particle Swarm Optimization Algorithm," Agriculture, MDPI, vol. 13(5), pages 1-18, May.
    3. Adeel Bashir & Sikandar Khan & Naveed Iqbal & Salem Bashmal & Sami Ullah & Fayyaz & Muhammad Usman, 2023. "A Review of the Various Control Algorithms for Trajectory Control of Unmanned Underwater Vehicles," Sustainability, MDPI, vol. 15(20), pages 1-21, October.

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