Skip to main content
SpringerLink
Log in

Potentials and Limitations of Autonomously Controlled Production Systems

  • Chapter
  • First Online:
Autonomous Cooperation and Control in Logistics

Abstract

The application of autonomous control to production logistic systems has already shown promising results. Especially, in highly dynamic situations autonomous control outperforms conventional production planning and control methods. However, the implementation of autonomous control to production systems seems to be unsuitable to some situations. It appears that classical planning methods perform best in well-defined situations with less dynamics. This contribution addresses the potentials and the limitations of autonomous control compared to centralized planning and controll algorithms. Therefore, scenarios of the flexible flow shop problem with varying degrees of complexity and dynamics are used for evaluating autonomous control methods. The results are compared to classical scheduling algorithms for the flexible flow shop scheduling, in order to identify limitations and potentials of autonomous control.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 169.99
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. Allahverdi A, Ng CT, Cheng TCE, Kovalyov M (2008) A survey of scheduling problems with setup times or costs. Eur J Oper Res 187(3):985–1032

    Article  MathSciNet  MATH  Google Scholar 

  2. Armbruster D, de Beer C, Freitag M, Jagalski T, Ringhofer C (2006) Autonomous control of production networks using a pheromone approach. Phys A 363(1):104–114

    Article  Google Scholar 

  3. Garey MR, Johnson DS (1979) Computers and intractability: a guide to the theory of NP-completeness. Freeman, San Francisco

    MATH  Google Scholar 

  4. Jedermann R, Moehrke A, Lang W (2010) Supervision of banana transport by the intelligent container. In: Kreyenschmidt J (ed) Coolchain-management. 4th International Workshop. University Bonn, Bonn, 2010, pp 75–84

    Google Scholar 

  5. Jungwattanakit J, Reodecha M, Chaovalitwongse P, Werner F (2005) An evaluation of sequencing heuristics for flexible flowshop scheduling problems with unrelated parallel machines and dual criteria. Preprint series: 05–28 (MSC: 90B35) Otto-von-Guericke-Universität Magdeburg Germany

    Google Scholar 

  6. Jungwattanakit J, Reodecha M, Chaovalitwongse P, Werner F (2008) Algorithms for flexible flow shop problems with unrelated parallel machines, setup times, and dual criteria. Int J Adv Manuf Technol 37(3):354–370

    Article  Google Scholar 

  7. Jungwattanakit J, Reodecha M, Chaovalitwongse P, Werner F (2009) A comparison of scheduling algorithms for flexible flow shop problems with unrelated parallel machines, setup times, and dual criteria. Comput Oper Res 36(2):358–378

    Article  MathSciNet  MATH  Google Scholar 

  8. Kim JH, Duffie NA (2004) Backlog control for a closed loop PPC system. Ann CIRP 53(1):357–360

    Article  Google Scholar 

  9. Pinedo ML (2008). Scheduling – theory, algorithms, and systems. Springer, New York

    MATH  Google Scholar 

  10. Quadt D, Kuhn H (2007) A taxonomy of flexible flow line scheduling procedures. Eur J Oper Res 178(3):686–698

    Article  MathSciNet  MATH  Google Scholar 

  11. Rekersbrink H, Makuschewitz T, Scholz-Reiter B (2009) A distributed routing concept for vehicle routing problems. Logist Res 1(1):45–52

    Article  Google Scholar 

  12. Rekersbrink H, Scholz-Reiter B, Zabel C (2010) An autonomous control concept for production logistics. In: Dangelmaier W et al (eds) Advanced manufacturing and sustainable logistics, pp 245–256. Springer, Berlin

    Chapter  Google Scholar 

  13. Scholz-Reiter B, Freitag, M, de Beer C, Jagalski T (2005) Modelling and analysis of autonomous shop floor control. Proceedings of 38th CIRP International Seminar on Manufacturing Systems. Florianopolis, Brazil

    Google Scholar 

  14. Scholz-Reiter B, Jagalski T, Bendul J (2007) Autonomous control of a shop floor based on bee’s foraging behaviour. In: Haasis HD, Kreowski HJ, Scholz-Reiter B. (eds) First International Conference on Dynamics in logistics. LDIC 2007. Springer, Berlin, Heidelberg, pp 415–423

    Google Scholar 

  15. Scholz-Reiter B, Görges M, Philipp T (2009) Autonomously controlled production systems – influence of autonomous control level on logistic performance. CIRP Ann Manuf Technol 58(1):395–398

    Article  Google Scholar 

  16. Scholz-Reiter B, Dashkovskiy S, Görges M, Naujok L (2010) Stability analysis of autonomously controlled production networks. Int J Prod Res. http://www.informaworld.com/10.1080/00207543.2010.505215 (accessed 07 December 2010)

  17. Scholz-Reiter B, Görges M, Jagalski T, Naujok L (2010) Modelling and analysis of an autonomous control method based on bacterial chemotaxis. 43rd CIRP International Conference on Manufacturing Systems 2010 (ICMS 2010). Neuer Wissenschaftlicher, Wien, pp 699–706

    Google Scholar 

  18. Scholz-Reiter B, Rekersbrink H, Görges M (2010) Dynamic flexible flow shop problems – scheduling heuristics vs. autonomous control. CIRP Ann Manuf Technol 59(1):465–468

    Google Scholar 

  19. Schönberger J, Kopfer H (2009) Online decision making and automatic decision model adaptation. Comput Oper Res 36(6):1740–1750

    Article  MATH  Google Scholar 

  20. Sung CS, Kim YH (2002) Minimizing makespan in a two-machine flowshop with dynamic arrivals allowed. Comput Oper Res 29(3):275–294

    Article  MathSciNet  MATH  Google Scholar 

  21. Ueda K, Hatono I, Fujii N, Vaario J (2000) “Reinforcement learning approaches to biological manufacturing systems”. CIRP Ann Manuf Technol 49(1):343–346

    Article  Google Scholar 

  22. Ueda K, Markus A, Monostori L, Kals HHJ, Arai T (2001) Emergent synthesis methodologies for manufacturing. CIRP Ann Manuf Technol 50(2):535–551

    Article  Google Scholar 

  23. Vaario J, Ueda, K. (1997) An emergent modelling method for dynamic scheduling. J Intell Manuf 9(2):129–140

    Article  Google Scholar 

  24. Windt K, Hülsmann M (2007) Changing paradigms in logistics – understanding the shift from conventional control to autonomous cooperation and control. In: Hülsmann M, Windt K (eds) Understanding autonomous cooperation & control – the impact of autonomy on management, information, communication, and material flow. Springer, Berlin, pp 4–16

    Google Scholar 

  25. Windt K, Böse F, Philipp T (2008) Autonomy in production logistics – identification, characterisation and application. Int J Robot CIM 24(4):572–578

    Article  Google Scholar 

  26. Windt K, Becker T (2009) Applying autonomous control methods in different logistic processes – a comparison by using an autonomous control application matrix. Proceedings of the 17th Mediterranean Conference on Control and Automation. Thessaloniki, Greece

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Planning and Control of Production Systems, BIBA, University of Bremen, Bremen, Germany

    Bernd Scholz-Reiter, Michael Görges & Henning Rekersbrink

Authors
  1. Bernd Scholz-Reiter
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Michael Görges
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Henning Rekersbrink
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. , Systems Management - International Logis, Jacobs University Bremen, Campus Ring 1, Bremen, 28759, Germany

    Michael Hülsmann

  2. BIBA GmbH, Bremer Institut für Messtechnik, Universität Bremen, Hochschulring 20, Bremen, 28359, Germany

    Bernd Scholz-Reiter

  3. , Global Production Logistics - Internatio, Jacobs University Bremen, Campus Ring 1, Bremen, 28759, Germany

    Katja Windt

Rights and permissions

Reprints and permissions

Copyright information

© 2011 Springer-Verlag Berlin Heidelberg

About this chapter

Cite this chapter

Scholz-Reiter, B., Görges, M., Rekersbrink, H. (2011). Potentials and Limitations of Autonomously Controlled Production Systems. In: Hülsmann, M., Scholz-Reiter, B., Windt, K. (eds) Autonomous Cooperation and Control in Logistics. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-19469-6_11

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-19469-6_11

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-19468-9

  • Online ISBN: 978-3-642-19469-6

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation