Cooperation Between Smart Manufacturing Scheduling Systems and Energy Providers: A Multi-agent Perspective

  • Maroua NouiriEmail author
  • Damien Trentesaux
  • Abdelghani Bekrar
  • Adriana Giret
  • Miguel A. Salido
Conference paper
Part of the Studies in Computational Intelligence book series (SCI, volume 803)


Within the emerging industrial sustainability domain, production efficiency interventions are gaining practical interest since manufacturing plants are facing increasing pressure to reduce their carbon footprint, driven by concerns related to energy costs and climate changes. This work focuses on the challenging issue of energy aware production scheduling and rescheduling systems (EAPSRS). The proposed multi-agent architecture (MA-EAPSRS) is hybrid, combining the predictive and the reactive phase while taking into account sustainability in both parts. It is composed of two cooperating multi-agent systems: the first one represents the smart manufacturing plant and the second one is the smart energy supply plant. It is based on interactions and negotiations between factory schedulers and energy providers. Uncertainties in term of machine’s disruptions and variation of processing time and in term of energy availability are also considered. In order to assess the proposed approach, an illustrative case study addressing the problem is presented and discussed.


Multi-agent system Future factories Sustainability Energy aware scheduling and rescheduling Energy consumption PSO 



The ELSAT2020 project is co-financed by the European Union with the European Regional Development Fund, the French state and the Hauts de France Region Council. This work was also partially funded by the Spanish research projects TIN2016-80856-R and TIN2015-65515-C4-1-R.


  1. 1.
    Baykasoglu, A.: Linguistic-based meta-heuristic optimization model for flexible job shop scheduling. Int. J. Prod. Res. 40(17), 4523–4543 (2002)CrossRefGoogle Scholar
  2. 2.
    Bilge, P., Badurdeen, F., Seliger, G., Jawahir, I.: A novel manufacturing architecture for sustainable value creation. CIRP Ann. 65(1), 455–458 (2016)CrossRefGoogle Scholar
  3. 3.
    Giret, A., Trentesaux, D., Salido, M.A., Garcia, E., Adam, E.: A holonic multi-agent methodology to design sustainable intelligent manufacturing control systems. J. Clean. Prod. 167(C), 1370–1386 (2017)CrossRefGoogle Scholar
  4. 4.
    Gonzalez, M., Oddi, A., Rasconi, R.: Multi-objective optimization in a job shop with energy costs through hybrid evolutionary techniques. In: Twenty-Seventh International Conference on Automated Planning and Scheduling, USA, 18–23 June 2017, pp. 140–148 (2017)Google Scholar
  5. 5.
    He, Y., Li, Y., Wu, T., Sutherland, J.W.: An energy-responsive optimization method for machine tool selection and operation sequence in flexible machining job shops. J. Clean. Prod. 87, 245–254 (2015)CrossRefGoogle Scholar
  6. 6.
    Kennedy, J., Eberhart, R.: Particle swarm optimization. In: IEEE International Conference on Neural Networks, Australia, 27 November–1 December 1995, pp. 1942–1948 (1995)Google Scholar
  7. 7.
    Liao, L.M., Huang, C.J.: A multi-agent based rescheduling framework for mixed-model assembly line balancing. In: IEEE International Conference on Industrial Engineering and Engineering Management, China, 10–13 December 2012, pp. 474–478 (2012)Google Scholar
  8. 8.
    May, G., Barletta, I., Stahl, B., Taisch, M.: Energy management in production: a novel method to develop key performance indicators for improving energy efficiency. Appl. Energy 149, 46–61 (2015)CrossRefGoogle Scholar
  9. 9.
    May, G., Stahl, B., Taisch, M.: Energy management in manufacturing: toward eco-factories of the future a focus group study. Appl. Energy 164, 628–638 (2016)CrossRefGoogle Scholar
  10. 10.
    Nouiri, M., Bekrar, A., Jemai, A., Niar, S., Ammari, A.C.: An effective and distributed particle swarm optimization algorithm for flexible job-shop scheduling problem. J. Intell. Manuf. 29(3), 603–615 (2018)CrossRefGoogle Scholar
  11. 11.
    Nouiri, M., Bekrar, A., Trentesaux, D.: Towards energy efficient scheduling and rescheduling for dynamic flexible job shop problem. In: Proceedings of the IFAC Symposium on Information Control Problems in Manufacturing, Bergamo, Italy, 11–13 June 2018, vol. 51, no. 11, pp. 1275–1280 (2018)Google Scholar
  12. 12.
    Nouiri, M., Jemai, A., Ammari, A.C., Bekrar, A., Niar, S.: An effective particle swarm optimization algorithm for flexible job-shop scheduling problem. In: IEEE International Conference on Industrial Engineering and Systems Management, Morocco, 28–30 October 2013, pp. 1–6 (2013)Google Scholar
  13. 13.
    Paolucci, M., Anghinol, D., Tonelli, F.: Facing energy-aware scheduling: a multi-objective extension of a scheduling support system for improving energy efficiency in a moulding industry. Soft Comput. 21, 3687–3698 (2017)CrossRefGoogle Scholar
  14. 14.
    Plitsos, S., Repoussis, P.P., Mourtos, I., Tarantilis, C.D.: Energy-aware decision support for production scheduling. Decis. Support Syst. 93, 88–97 (2017)CrossRefGoogle Scholar
  15. 15.
    Raileanu, S., Anton, F., Iatan, A., Borangiu, T., Anton, S., Morariu, O.: Resource scheduling based on energy consumption for sustainable manufacturing. J. Intell. Manuf. 28(7), 1519–1530 (2017)CrossRefGoogle Scholar
  16. 16.
    Salido, M.A., Joan, E., Federico, B., Giret, A.: Rescheduling in job-shop problems for sustainable manufacturing systems. J. Clean. Prod. 162(20), S121–S132 (2016)Google Scholar
  17. 17.
    Tonelli, F., Bruzzone, A., Paolucci, M., Carpanzano, E., Nicolo, G., Giret, A., Salido, M., Trentesaux, D.: Assessment of mathematical programming and agent-based modelling for offline scheduling: application to energy aware manufacturing. CIRP Ann. Manuf. Technol. 65(1), 405–408 (2016)CrossRefGoogle Scholar
  18. 18.
    Trentesaux, D., Giret, A., Tonelli, F., Skobelev, P.: Emerging key requirements for future energy-aware production scheduling systems: a multi-agent and holonic perspective. In: Service Orientation in Holonic and Multi-Agent Manufacturing. Studies in Computational Intelligence, vol. 694, pp. 127–141 (2016)Google Scholar
  19. 19.
    Zhang, L., Li, X., Gao, L., Zhang, G.: Dynamic rescheduling in FMS that is simultaneously considering energy consumption and schedule efficiency. Int. J. Adv. Manuf. Technol. 87(5–8), 1387–1399 (2013)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Maroua Nouiri
    • 1
    Email author
  • Damien Trentesaux
    • 1
  • Abdelghani Bekrar
    • 1
  • Adriana Giret
    • 2
  • Miguel A. Salido
    • 2
  1. 1.LAMIH Laboratory, UMR CNRS 8201, University of Valenciennes and Hainaut Cambrésis (UVHC)ValenciennesFrance
  2. 2.Dpto. Sistemas Informáticos y ComputaciónUniversitat Politècnica de ValènciaValenciaSpain

Personalised recommendations