Skip to main content

Abstract

The purpose of this paper is to present an agents-based methodology that allows for the creation and optimization of schedule while taking into account a wide range of constraints or preferences. When some smart households benefit from a common energy source, if the available power is limited, the problem to be solved for improving energy efficiency is how to program the power-on time of the peripherals according to the power limits and taking into account the preferences of the users. The proposed operating system was developed as multi-agent systems (MAS) on the JADE platform. The implementation is discussed by describing in detail each agent and the control algorithm. In addition, complementary metrics are proposed, to evaluate the performance of the planning method. Finally, to illustrate the proposed method, some simulation results are presented.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 189.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 249.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 249.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Redl, T.A.: On using graph coloring to create university schedules with essential and preferential conditions. http://cms.uhd.edu/faculty/redlt/iccis09proc.pdf

  2. Nachtigall, K., pitz, J.: A modulo network simplex method for solving periodic schedule optimisation problems. In: Operations Research Proceedings (2007)

    Google Scholar 

  3. Genetic Algorithms Overview: geneticalgorithms.ai-depot.com/Tutorial/Overview.html

  4. Kragelund, L.V.: Solving a timetabling problem using hybrid genetic algorithms. Softw. Pract. Exper. 27(10), 1121–1134 (1996)

    Google Scholar 

  5. Cardoen, B., Demeulemeester, E., Beliën, J.: Optimizing a multiple objective surgical case sequencing problem. Int. J. Prod. Econ. 119(2), 354–366 (2009)

    Article  MATH  Google Scholar 

  6. Cardoen, B., Demeulemeester, E., Beliën, J.: Sequencing surgical cases in a day-care environment: an exact branch-and-price approach. Comput. Oper. Res. 36(9), 2660–2669 (2009)

    Article  MATH  Google Scholar 

  7. Cardoen, B., Demeulemeester, E., Beliën, J.: Operating room planning and scheduling: A literature review. Eur. J. Oper. Res. 201(3), 921–932 (2010)

    Article  MATH  Google Scholar 

  8. Dekhici, L., Belkadi, K.: Operating theatre scheduling under constraints. J. Appl. Sci. 10(14), 1380–1388 (2010)

    Article  Google Scholar 

  9. Saleh, B.B., El Moudni, A., Hajjar, M., Barakat, O.: A multi-agent architecture for dynamic scheduling of emergencies in operating theater. In: Advances in Intelligent Systems and Computing, vol. 869. Springer, Cham (2019)

    Google Scholar 

  10. Saleh, B.B., El Moudni, A., Hajjar, M., Barakat, O.: Towards an integral operating room management system (2018). ieeexplore.ieee.org/document/8394877

  11. Tkaczyk, R., Ganzha, M., Paprzycki, M.: Agent-planner agent, based timetabling system. Informatica 40(1), (2016)

    Google Scholar 

  12. Saleh, B.B., El Moudni, A., Hajjar, M., Barakat, O.: A cooperative control model for operating theater scheduling (2018). ieeexplore.ieee.org/document/8394888/

  13. Koutsopoulos, I., Hatzi, V.: Optimal energy storage control policies for the smart power grid. In: 2011 IEEE International Conference on Smart Grid Communications, pp. 475–480

    Google Scholar 

  14. Praça, I., Ramos, C., Vale, Z., Cordeiro, M.: Intelligent agents for negotiation and game-based decision support in electricity markets. researchgate.net/publication/267806879

  15. Petersen, J., Shunturov, V., Janda, K.: Dormitory residents reduce electricity consumption when exposed to real time visual feedback and incentives. Int. J. Sustain. High. Educ. 8(1), 16–33 (2007)

    Article  Google Scholar 

  16. Gonzalez-Romera, E., Jaramillo-Moran, M.A., Carmona, D.: Forecasting of the electric energy demand trend and monthly fluctuation with neural networks. Comput. Ind. Eng. 52(3), 336–343 (2007)

    Article  Google Scholar 

  17. Carabelea, C., Boissier, O., Ramparany, F.: Benefits and requirements of using multi-agent systems on smart devices. Lecture Notes in Computer Science (2003)

    Google Scholar 

  18. Marik, V., Stepankova, O., Lazansky, J.: Artificial intelligence. In: J.ICIE 2015 3rd International Conference on Innovation and Entrepreneurship

    Google Scholar 

  19. Budinská, I., Dang, T.T.: A case based reasoning in a multi agents support system. In: Proceedings of the 6th International Scientific-Technical Conference, Process Control (2004)

    Google Scholar 

  20. Dang, T.T.: Improving plan quality through agent coalitions. In: IEEE International Conference on Computational Cybernetics – ICCC (2004)

    Google Scholar 

  21. Druiven, S.: Knowledge development in games of imperfect information. University Maastricht Master Thesis, Institute for Knowledge and Agent Technology, University Maastricht (2002)

    Google Scholar 

  22. JADE. http://jade.tilab.com/

  23. FIPA. http://www.fipa.org/

  24. FIPA (2002) FIPA ACL Message Structure Specification. SC00061G

    Google Scholar 

  25. Dounis, A.I.: Artificial intelligence for energy conservation in buildings. Adv. Build. Energy Res. 4(1), 267–299 (2010)

    Article  Google Scholar 

  26. Pynadath, D.V., Tambe, M.: Multiagent teamwork: analyzing the optimality and complexity of key theories and models, pp. 873–880. ACM (2002)

    Google Scholar 

  27. Scerri, P., Pynadath, D.V., Tambe, M.: Towards adjustable autonomy for the real world. J. Artif. Intell. Res. 17, 171–228 (2002)

    Article  MathSciNet  MATH  Google Scholar 

  28. Statistisches Bundesamt, “Wirtschaftsbereich energie - erzeugung,” Statistisches Bundesamt, Technical Report, 2017

    Google Scholar 

  29. NIST: Roadmap for smart grid interoperability standards, vol. 1108. NIST Special Publication (2010)

    Google Scholar 

  30. Bruinenberg, J., et al.: Smart grid coordination group technical report reference architecture for the smart grid version 1.0 (draft) 2012-03-02. Technical Report (2012)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding authors

Correspondence to Bilal Bou Saleh , Ghazi Bou Saleh , Mohammad Hajjar , Abdellah El Moudni or Oussama Barakat .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Bou Saleh, B., Bou Saleh, G., Hajjar, M., El Moudni, A., Barakat, O. (2020). Multi-agents Planner for Assistance in Conducting Energy Sharing Processes. In: Bouhlel, M., Rovetta, S. (eds) Proceedings of the 8th International Conference on Sciences of Electronics, Technologies of Information and Telecommunications (SETIT’18), Vol.1. SETIT 2018. Smart Innovation, Systems and Technologies, vol 146. Springer, Cham. https://doi.org/10.1007/978-3-030-21005-2_43

Download citation

Publish with us

Policies and ethics