Delegate MASs for coordination and control of one-directional AGV systems: a proof-of-concept

  • Branislav Micieta
  • Milan Edl
  • Martin Krajcovic
  • Luboslav Dulina
  • Peter Bubenik
  • Lukas Durica
  • Vladimira BinasovaEmail author


Decentralized coordination and route planning face the challenges such as scalability, dynamic changes (disturbances) in the environment, continuous planning, and coordination issues (i.e., deadlock and livelock situations). Self-organized delegate multi-agent systems (D-MASs) have proven to be effective decentralized coordination mechanisms for coordination and control (C&C) applications. However, the use of such coordination mechanisms becomes more challenging, compared to the previous studies, in which the coordinated entities are one-directional automated guided vehicles (AGVs), with restricted movement, situated in a highly dynamic production environment. To address these challenges, there were several problematic situations identified dealing with issues such as the originally proposed functionalities of D-MASs, restricted movement, priority parameter settings, and simulated failures of AGVs. Solutions (coordination rules) to these situations were proposed, also described examples were provided and, finally, the approach was verified by simulation in the 3D environment, involving five AGV agents (AGVAs). Simple indicators of such intralogistics system were proposed to outline the system performance. Simulations were performed with as well as without simulated failure states. Simulation results show that the proof-of-concept was reached, and that by the combination of the proposed coordination rules and D-MAS, one-directional AGVAs were able to generate a short-term forecast for the near future and thus anticipate and avoid coordination issues as well as to cope with simulated failures.


Delegated multi-agent systems Automated guided vehicle Coordination and control Decentralized systems Short-term forecasting Manufacturing planning Optimization and simulation 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Supplementary material


00:00 The forecast is cyclically created by intention ant colonies and is based on a result of the exploration ant colony. Forecasts are made by reservation pheromones dropped in the virtual environment. This mechanism allows to create predictions, possible future states of the system, and then to proactively react and avoid possible deadlocks and livelocks. 02:20 Visualizing forecasts of multiple AGV agents. 3:15 Without visualization of forecasts (WMV 219546 kb)

170_2017_915_MOESM2_ESM.docx (14 kb)
ESM 2 (DOCX 13 kb)


  1. 1.
    Parunak HVD (1996) Foundations of distributed artificial intelligence. In: O’Hare GMP, Jennings NR (eds) Found. Distrib. Artif. Intell. John Wiley & Sons, New York, pp 139–164Google Scholar
  2. 2.
    Shen W, Norrie DH (1999) Agent-based systems for intelligent manufacturing: a state-of-the-art survey. Knowl Inf Sys Int J 1:129–156. doi: 10.1007/BF03325096 CrossRefGoogle Scholar
  3. 3.
    Leitao P, Marik V, Vrba P (2013) Past, present, and future of industrial agent applications. IEEE Trans Ind Inf 9:2360–2372. doi: 10.1109/TII.2012.2222034 CrossRefGoogle Scholar
  4. 4.
    Parunak HVD, Raymond V (1997) Managing emergent behavior in distributed control systems potential for emergent behavior. In: ISA-tech ‘97. Anaheim, pp 1–8Google Scholar
  5. 5.
    Vrba P, Tichy P, Marik V et al (2011) Rockwell automation’s Holonic and multiagent control systems compendium. IEEE Trans Syst Man Cybern Part C Appl Rev 41:14–30. doi: 10.1109/TSMCC.2010.2055852 CrossRefGoogle Scholar
  6. 6.
    Barbosa J, Leitao P, Adam E, Trentesaux D (2015) Dynamic self-organization in Holonic multi-agent manufacturing systems: the ADACOR evolution. Comput Ind 66:99–111. doi: 10.1016/j.compind.2014.10.011 CrossRefGoogle Scholar
  7. 7.
    Valckenaers P, Van Brussel H (2005) Holonic manufacturing execution systems. CIRP Ann - Manuf Technol 54:427–432. doi: 10.1016/S0007-8506(07)60137-1 CrossRefGoogle Scholar
  8. 8.
    Parunak HVD, Brueckner S (2007) Concurrent modeling of alternative worlds with polyagents. In: Multi-Agent-Based Simul. VII. Springer Berlin Heidelberg, Berlin, Heidelberg, pp 128–141Google Scholar
  9. 9.
    Fox B, Xiang W, Lee HP (2007) Industrial applications of the ant colony optimization algorithm. Int J Adv Manuf Technol 31:805–814. doi: 10.1007/s00170-005-0254-z CrossRefGoogle Scholar
  10. 10.
    Holvoet T, Valckenaers P (2006) Exploiting the environment for coordinating agent intentions. In: Environ. Multi-Agent Syst. III. Springer Berlin Heidelberg, Berlin, Heidelberg, pp 51–66Google Scholar
  11. 11.
    Weyns D, Holvoet T, Helleboogh A (2007) Anticipatory vehicle routing using delegate multi-agent systems. IEEE Intell Transp Syst Conf IEEE 87–93. doi:  10.1109/ITSC.2007.4357809
  12. 12.
    McCann HJ, Bratman ME (1991) Intention, plans, and practical reason. Nous 25:230. doi: 10.2307/2215590 CrossRefGoogle Scholar
  13. 13.
    Rao AS, Georgeff MP (1995) BDI-agents: from theory to practice. Proc. first Intl. Conf. Multiagent Syst. San Francisco, pp 312–329. Accessed 26 March 2016
  14. 14.
    Van Belle J, Philips J, Ali O et al (2012) A service-oriented approach for Holonic manufacturing control and beyond. In: Borangiu T, Trentesaux D, Thomas A (eds) Serv. Orientat. Holonic Multi-Agent Manuf. Control. Springer, Berlin Heidelberg, pp 1–20Google Scholar
  15. 15.
    Steegmans E, Holvoet T, Janssens N et al (2002) Ant algorithms in a graph environment: a meta-scheme for coordination and control. In: Hanza M (ed) Artif. Intell. Appl. ACTA Press, Calgary, pp 435–440Google Scholar
  16. 16.
    Holvoet T, Weyns D, Valckenaers P (2009) Patterns of delegate MAS. In: 2009 third IEEE Int. Conf. Self-Adaptive Self-Organizing Syst. IEEE, pp 1–9. doi:  10.1109/SASO.2009.31
  17. 17.
    Maes P (1990) Situated agents can have goals. Rob Auton Syst 6:49–70. doi: 10.1016/S0921-8890(05)80028-4 CrossRefGoogle Scholar
  18. 18.
    Holvoet T, Weyns D, Valckenaers P (2010) Delegate MAS patterns for large-scale distributed coordination and control applications. In: Proc. 15th Eur. Conf. Pattern Lang. Programs - Eur. ‘10. ACM Press, New York, p 1Google Scholar
  19. 19.
    Cruz Torres MH, Van Beers T, Holvoet T (2011) (No) more design patterns for multi-agent systems. Proc Compil. Co-located work. doi: 10.1145/2095050.2095083
  20. 20.
    Breton L, Maza S, Castagna P (2006) A multi-agent based conflict-free routing approach of bi-directional automated guided vehicles. In: 2006 Am. Control Conf. IEEE, pp 2825–2830Google Scholar
  21. 21.
    Hamzheei M, Farahani RZ, Rashidi-Bajgan H (2013) An ant colony-based algorithm for finding the shortest bidirectional path for automated guided vehicles in a block layout. Int J Adv Manuf Technol 64:399–409. doi: 10.1007/s00170-012-3999-1 CrossRefGoogle Scholar
  22. 22.
    Marino D, Fagiolini A, Pallottino L (2011) Distributed collision-free protocol for AGVs in industrial environments. 1–10. Accessed 20 March 2016
  23. 23.
    Durica L, Micieta B, Bubenik P, Binasova V (2015, 2015) Manufacturing multi-agent system with bio-inspired techniques: codesa-prime. MM Sci J:829–837. doi: 10.17973/MMSJ.2015_12_201543
  24. 24.
    Durica L (2016) Multi-agent logistics system implemented in virtual reality. University of ZilinaGoogle Scholar
  25. 25.
    Haluska M, Gregor M (2016) Concept of the system for design and optimization of configurations in new generation of manufacturing systems. Int J Manag Soc Sci Res Rev 1:181–184 Accessed 26 March 2016Google Scholar
  26. 26.
    Leitão P (2013) Towards self-organized service-oriented multi-agent systems. Springer, Berlin Heidelberg, pp 41–56Google Scholar
  27. 27.
    Jafari D, Moattar Husseini SM, Fazel Zarandi MH, Zanjirani Farahani R (2009) Coordination of order and production policy in buyer–vendor chain using PROSA Holonic architecture. Int J Adv Manuf Technol 45:1033–1050. doi: 10.1007/s00170-009-2039-2 CrossRefGoogle Scholar
  28. 28.
    Leitão P, Restivo F (2005) ADACOR: a Holonic architecture for agile and adaptive manufacturing control. doi:  10.1016/j.compind.2005.05.005
  29. 29.
    McFarlane D, Sarma S, Chirn JL et al (2003) Auto ID systems and intelligent manufacturing control. Eng Appl Artif Intell 16:365–376. doi: 10.1016/S0952-1976(03)00077-0 CrossRefGoogle Scholar
  30. 30.
    Karkkainen M, Holmstrom J, Framling K, Artto K (2003) Intelligent products—a step towards a more effective project delivery chain. Comput Ind 50:141–151. doi: 10.1016/S0166-3615(02)00116-1 CrossRefGoogle Scholar
  31. 31.
    Venta O (2007) Intelligent products and systems technology theme—final report. HelsinkiGoogle Scholar
  32. 32.
    Meyer GG, Främling K, Holmström J (2009) Intelligent products: a survey. Comput Ind 60:137–148. doi: 10.1016/j.compind.2008.12.005 CrossRefGoogle Scholar
  33. 33.
    Ella Platform - smart solution (2016) Ella Platform - Edgecom 1Google Scholar
  34. 34.
    Aridor Y, Lange DB (1998) Agent design patterns: elements of agent application design. In: Proc. second Int. Conf. Auton. AGENTS - AGENTS ‘98. ACM press, New York, pp 108–115Google Scholar

Copyright information

© Springer-Verlag London Ltd. 2017

Authors and Affiliations

  • Branislav Micieta
    • 1
  • Milan Edl
    • 2
  • Martin Krajcovic
    • 1
  • Luboslav Dulina
    • 1
  • Peter Bubenik
    • 1
  • Lukas Durica
    • 1
  • Vladimira Binasova
    • 1
    Email author
  1. 1.Faculty of Mechanical Engineering, Department of Industrial EngineeringUniversity of ZilinaZilinaSlovak Republic
  2. 2.Department of Industrial Engineering and ManagementUniversity of West BohemiaPlzenCzech Republic

Personalised recommendations