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CoMM: a consensus algorithm for multi-agent-based manufacturing system to deal with perturbation

  • Tsegay Tesfay MezgebeEmail author
  • Guillaume Demesure
  • Hind Bril El Haouzi
  • Remi Pannequin
  • Andre Thomas
ORIGINAL ARTICLE

Abstract

The emergence of Cyber Physical System has dramatically impacted the use of traditionally centralized control system in responding to unexpected events. Rush order is a quite common unexpected event in the current dynamic market characteristics and has significant perturbing ability to a centrally predictive schedule. This paper is aimed to propose a consensus algorithm for multi-agent-based manufacturing system (CoMM) to control the rush order and henceforth minimize a makespan. Consensus is an algorithmic procedure applied in control theory which allows convergence of state between locally autonomous agents collaborating for their common goal. Leader-follower communication approach was used among the multi-agent to deal with the perturbing event. Each agent decides when to broadcast its state to neighbor agents, and the controlling decision depends on the behavior of this state. The consensus algorithm is initially modeled by networking all contributing agents. After this, it is validated with simulation experiment based on academic full-sized ap plication platform called TRACILOGIS platform. The results showed that the consensus algorithm has significantly minimized the impact of rush order on makespan of manufacturing orders launched on a system.

Keywords

Consensus algorithm State Multi-agent system Convergence Rush order Makespan 

Notes

Acknowledgments

The authors gratefully acknowledge the financial support of the CPER 2015-2020 Projet Cyber-Entreprises du programme Sciences du numérique, through regional (Région Lorraine, Grand EST), national (DRRT, CNRS, INRIA), and European (FEDER) funds used to extend The TRACILOGIS Platform.

References

  1. 1.
    El Haouzi H, Pétin JF, Thomas A (2009) Design and validation of a product-driven control system based on a six sigma methodology and discrete event simulation. Prod Plan Control 20(6):510–524CrossRefGoogle Scholar
  2. 2.
    Rey GZ, Bonte T, Prabhu V, Trentesaux D (2014) Reducing myopic behavior in FMS control: a semi-heterarchical simulation–optimization approach. Simul Model Pract Theory 46:53–75CrossRefGoogle Scholar
  3. 3.
    Trentesaux D (2009) Distributed control of production systems. Eng Appl Artif Intell 22(7):971–978CrossRefGoogle Scholar
  4. 4.
    Jimenez JF, Bekrar A, Zambrano-Rey G, Trentesaux D, Leitão P (2017) Pollux: a dynamic hybrid control architecture for flexible job shop systems. Int J Prod Res 55(15):4229–4247CrossRefGoogle Scholar
  5. 5.
    Ehteshami B, Petrakian RG, Shabe PM (1992) Trade-offs in cycle time management: hot lots. IEEE Trans Semicond Manuf 5(2):101–106CrossRefGoogle Scholar
  6. 6.
    Wang WP, Chen Z (2008) A neuro-fuzzy based forecasting approach for rush order control applications. Expert Syst Appl 35(1–2):223–234CrossRefGoogle Scholar
  7. 7.
    Trzyna D, Kuyumcu A, Lödding H (2012) Throughput time characteristics of rush orders and their impact on standard orders. Procedia CIRP 3:311–316.  https://doi.org/10.1016/j.procir.2012.07.054 CrossRefGoogle Scholar
  8. 8.
    Leitão P (2009) Agent-based distributed manufacturing control: a state-of-the-art survey. Eng Appl Artif Intell 22(7):979–991CrossRefGoogle Scholar
  9. 9.
    Isern D, Sánchez D, Moreno A (2011) Organizational structures supported by agent-oriented methodologies. J Syst Softw 84(2):169–184CrossRefGoogle Scholar
  10. 10.
    Xiong W, Fu D (2018) A new immune multi-agent system for the flexible job shop scheduling problem. J Intell Manuf 29(4):857–873CrossRefGoogle Scholar
  11. 11.
    Caridi M, Cavalieri S (2004) Multi-agent systems in production planning and control: an overview. Prod Plan Control 15(2):106–118CrossRefGoogle Scholar
  12. 12.
    Wooldridge M (2009) An introduction to MultiAgent systems. Wiley, LiverpoolGoogle Scholar
  13. 13.
    Rey GZ, Pach C, Aissani N, Bekrar A, Berger T, Trentesaux D (2013) The control of myopic behavior in semi-heterarchical production systems: a holonic framework. Eng Appl Artif Intell 26(2):800–817CrossRefGoogle Scholar
  14. 14.
    Tonino H, Bos A, de Weerdt M, Witteveen C (2002) Plan coordination by revision in collective agent based systems. Artif Intell 142(2):121–145MathSciNetzbMATHCrossRefGoogle Scholar
  15. 15.
    Rahwan I, Ramchurn SD, Jennings NR, Mcburney P, Parsons S, Sonenberg L (2003) Argumentation-based negotiation. Knowl Eng Rev 18(4):343–375CrossRefGoogle Scholar
  16. 16.
    Pannequin R, Thomas A (2012) Another interpretation of stigmergy for product-driven systems architecture. J Intell Manuf 23(6):2587–2599CrossRefGoogle Scholar
  17. 17.
    Valckenaers P, Van Brussel H (2016) Design for the Unexpected: from Holonic manufacturing systems towards a humane mechatronics society, OxfordGoogle Scholar
  18. 18.
    Leitão P, Barbosa J, Trentesaux D (2012) Bio-inspired multi-agent systems for reconfigurable manufacturing systems. Eng Appl Artif Intell 25(5):934–944CrossRefGoogle Scholar
  19. 19.
    Pach C, Berger T, Bonte T, Trentesaux D (2014) ORCA-FMS: a dynamic architecture for the optimized and reactive control of flexible manufacturing scheduling. Comput Ind 65(4):706–720CrossRefGoogle Scholar
  20. 20.
    Nagarajan M, Sošić G (2008) Game-theoretic analysis of cooperation among supply chain agents: review and extensions. Eur J Oper Res 187(3):719–745MathSciNetzbMATHCrossRefGoogle Scholar
  21. 21.
    Rosenschein JS, Zlotkin G (1994) Rules of encounter: designing conventions for automated negotiation among computers. MassachusettsGoogle Scholar
  22. 22.
    Kraus S (1997) Negotiation and cooperation in multi-agent environments. Artif Intell 94(1–2):79–97zbMATHCrossRefGoogle Scholar
  23. 23.
    Jennings NR, Faratin P, Lomuscio AR, Parsons S, Wooldridge MJ, Sierra C (2001) Automated negotiation: prospects, methods and challenges. Group Decis Negot 10(2):199–215CrossRefGoogle Scholar
  24. 24.
    Mezgebe TT, El Haouzi HB, Demesure D, Thomas A (2018) A negotiation-based control approach for disturbed industrial context. IFAC-Pap. 51(11):1255–1260CrossRefGoogle Scholar
  25. 25.
    Olfati-Saber R, Murray RM (2004) Consensus problems in networks of agents with switching topology and time-delays. IEEE Trans Autom Control 49(9):1520–1533MathSciNetzbMATHCrossRefGoogle Scholar
  26. 26.
    Cao Y, Yu W, Ren W, Chen G (2013) An overview of recent progress in the study of distributed multi-agent coordination. IEEE Trans Ind Inform 9(1):427–438CrossRefGoogle Scholar
  27. 27.
    Dimarogonas DV, Kyriakopoulos KJ (2007) On the rendezvous problem for multiple nonholonomic agents. IEEE Trans Autom Control 52(5):916–922MathSciNetzbMATHCrossRefGoogle Scholar
  28. 28.
    Sinha A, Ghose D (2006) Generalization of linear cyclic pursuit with application to rendezvous of multiple autonomous agents. IEEE Trans Autom Control 51(11):1819–1824MathSciNetzbMATHCrossRefGoogle Scholar
  29. 29.
    Lee D, Spong MW (2007) Stable flocking of multiple inertial agents on balanced graphs. IEEE Trans Autom Control 52(8):1469–1475MathSciNetzbMATHCrossRefGoogle Scholar
  30. 30.
    Olfati-Saber R (2006) Flocking for multi-agent dynamic systems: algorithms and theory. IEEE Trans Autom Control 51(3):401–420MathSciNetzbMATHCrossRefGoogle Scholar
  31. 31.
    Moore KL, Lucarelli D (2007) Decentralized adaptive scheduling using consensus variables. Int J Robust Nonlinear Control 17(10–11):921–940MathSciNetzbMATHCrossRefGoogle Scholar
  32. 32.
    Ogren P, Fiorelli E, Leonard NE (2004) Cooperative control of mobile sensor networks: adaptive gradient climbing in a distributed environment. IEEE Trans Autom Control 49(8):1292–1302MathSciNetzbMATHCrossRefGoogle Scholar
  33. 33.
    Su H, Wang X, Lin Z (2009) Synchronization of coupled harmonic oscillators in a dynamic proximity network. Automatica. 45(10):2286–2291MathSciNetzbMATHCrossRefGoogle Scholar
  34. 34.
    Ren W, Beard RW (2008) Distributed consensus in multi-vehicle cooperative control: theory and applications. Verlag, LondonzbMATHCrossRefGoogle Scholar
  35. 35.
    Wang X, Shao J (2015) Consensus for discrete-time multi-agent systems. Discrete Dyn Nat Soc 2015:1–6.  https://doi.org/10.1155/2015/380184 CrossRefGoogle Scholar
  36. 36.
    Cardin O, Trentesaux D, Thomas A, Castagna P, Berger T, El-Haouzi HB (2017) Coupling predictive scheduling and reactive control in manufacturing hybrid control architectures: state of the art and future challenges. J Intell Manuf 28(7):1503–1517CrossRefGoogle Scholar
  37. 37.
    Espejo R, Reyes A (2011) Organizational systems: managing complexity with the viable system model. Heidelberg Dordrecht, London New YorkGoogle Scholar

Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2019

Authors and Affiliations

  • Tsegay Tesfay Mezgebe
    • 1
    Email author
  • Guillaume Demesure
    • 1
  • Hind Bril El Haouzi
    • 1
  • Remi Pannequin
    • 1
  • Andre Thomas
    • 1
  1. 1.Campus SciencesUniversité de Lorraine, CRAN, UMR 7039Vandoeuvre-lès-Nancy CedexFrance

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