Skip to main content

Advertisement

SpringerLink
Log in

Redundant configuration of automated flow lines based on “Industry 4.0”-technologies

  • Original Paper
  • Published:
Journal of Business Economics Aims and scope Submit manuscript

Abstract

Over the past decades, robots have been heavily used for flow lines to increase productivity and product quality and to relieve workers of repetitive and dangerous tasks. However, despite continuous improvement of robots, the occurrence of failures remains a significant challenge in the operation of automated flow lines. Due to the connection of the stations in a flow line via a material handling system, failures at one station can quickly lead to throughput losses due to blocking and starving of upstream and downstream stations, respectively. To some extent, these throughput losses can be reduced by installing buffers between the stations. However, the installation of buffers requires considerable investments and scarce factory space. Therefore, the minimization of the total number of buffers is one of the primary objectives in flow line planning. Due to the advances of manufacturing technologies that form the foundation of “Industry 4.0”, new solutions to reduce throughput losses caused by equipment failures open up. One solution is a redundant configuration, in which downstream stations automatically take over the operations of failed stations in the event of failure. The throughput loss in these situations mainly depends on the level of redundancy designed into the system. Based on existing methods for the design of automated flow lines, we present two line balancing formulations for the configuration of automated flow lines under consideration of redundancies. The first formulation aims at maximizing the lines’ level of redundancy. The second formulation aims at a balanced allocation of redundancies along the line. To evaluate the presented formulations, we compare the performance with an existing line balancing approach for automated lines. With respect to this approach, improvements of the throughput rate between 3 % and 7 % are achieved.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

References

  • Aghajani M, Ghodsi R, Javadi B (2014) Balancing of robotic mixed-model two-sided assembly line with robot setup times. Int J Adv Manuf Technol 74(5–8):1005–1016

    Article  Google Scholar 

  • Battaïa O, Dolgui A (2013) A taxonomy of line balancing problems and their solution approaches. Int J Prod Econ 142(2):259–277

    Article  Google Scholar 

  • Baybars I (1986) A survey of exact algorithms for the simple assembly line balancing problem. Manage Sci 32(8):909–932

    Article  Google Scholar 

  • Becker C, Scholl A (2006) A survey on problems and methods in generalized assembly line balancing. Eur J Oper Res 168(3):694–715

    Article  Google Scholar 

  • Belmansour A-T, Nourelfath M (2012) Performance evaluation of a reconfigurable production line. Int J Perform Eng 8(4):427–435

    Google Scholar 

  • Boysen N, Fliedner M (2006) Ein flexibler zweistufiger Graphen-Algorithmus zur Fließbandabstimmung mit praxisrelevanten Nebenbedingungen. Zeitschrift für Betriebswirtschaft 76(1):55–78

    Article  Google Scholar 

  • Boysen N, Fliedner M, Scholl A (2007a) A classification of assembly line balancing problems. Eur J Oper Res 183(2):674–693

    Article  Google Scholar 

  • Boysen N, Fliedner M, Scholl A (2007b) Produktionsplanung bei Variantenfließfertigung: Planungshierarchie und Elemente einer Hierarchischen Planung. Zeitschrift für Betriebswirtschaft 77(7–8):759–793

    Article  Google Scholar 

  • Boysen N, Fliedner M, Scholl A (2008) Assembly line balancing: which model to use when? Int J Prod Econ 111(2):509–528

    Article  Google Scholar 

  • Brynjolfsson E, McAfee A (2014) The second machine age. Work, progress, and prosperity in a time of brilliant technologies. Norton, New York

    Google Scholar 

  • Bukchin J, Rubinovitz J (2003) A weighted approach for assembly line design with station paralleling and equipment selection. IIE Trans 35(1):73–85

    Article  Google Scholar 

  • Bukchin J, Tzur M (2000) Design of flexible assembly line to minimize equipment cost. IIE Trans 32(7):585–598

    Google Scholar 

  • Conway R, Maxwell W, McClain JO, Thomas LJ (1988) Role of work-in-process inventory in serial production lines. Oper Res 36(2):229–241

    Article  Google Scholar 

  • Daoud S, Chehade H, Yalaoui F, Amodeo L (2014) Solving a robotic assembly line balancing problem using efficient hybrid methods. J Heuristics 20(3):235–259

    Article  Google Scholar 

  • Dar-El EM (1973) MALB—a heuristic technique for balancing large single-model assembly lines. AIIE Trans 5(4):343–356

    Article  Google Scholar 

  • Deloitte (2015) Industry 4.0: Challenges and solutions for the digital transformation and use of exponential technologies. http://www2.deloitte.com/ch/en/pages/manufacturing/articles/manufacturing-study-industry-4.html. Accessed 3 Aug 2016

  • Evans PC, Annuziata M (2012) Industrial Internet: pushing the Boundaries of minds and machines. http://www.ge.com/docs/chapters/Industrial_Internet.pdf. Accessed 3 Aug 2016

  • Gao J, Sun L, Wang L, Gen M (2009) An efficient approach for type II robotic assembly line balancing problems. Comput Ind Eng 56(3):1065–1080

    Article  Google Scholar 

  • Graves SC, Lamar BW (1983) Integer programming procedure for assembly system design problems. Oper Res 31(3):522–545

    Article  Google Scholar 

  • Graves SC, Redfield CH (1988) Equipment selection and task assignment for multiproduct assembly system design. Int J Flex Manuf Syst 1(1):31–50

    Article  Google Scholar 

  • Hazir Ö, Dolgui A (2014) Robust assembly line balancing: state of the art and new research perspectives. In: Sotskov, Werner (eds) Sequencing and scheduling with inaccurate data. Nova Science Publishers, Inc, Hauppauge

    Google Scholar 

  • Helber S (2000) Kapitalwertorientierte Pufferallokation in stochastischen Fließproduktionssystemen. Schmalenbachs Zeitschrift für betriebswirtschaftliche Forschung: Zfbf 52(3):211–233

  • Helber S (2001) Cash-flow-oriented buffer allocation in stochastic flow lines. Int J Prod Res 39(14):3061–3083

    Article  Google Scholar 

  • Inman RR (1999) Empirical evaluation of exponential and independence assumptions in queueing models of manufacturing systems. Prod Oper Manag 8(4):409–432

    Article  Google Scholar 

  • International Federation of Robotics (2015) Industrial Robots Statistics. http://www.ifr.org/industrial-robots/statistics/. Accessed 3 Aug 2016

  • Kagermann H, Wahlster W, Helbig J (2013) Umsetzungsempfehlungen für das Zukunftsprojekt Industrie 4.0. Abschlussbericht des Arbeitskreises Industrie 4.0, Frankfurt/Main

  • Kahan T, Bukchin Y, Menassa R, Ben-Gal I (2009) Backup strategy for robots’ failures in an automotive assembly system. Int J Prod Econ 120(2):315–326

    Article  Google Scholar 

  • Kim H, Park S (1995) Strong cutting plane algorithm for the robotic assembly line balancing problem. Int J Prod Res 33(8):2311–2323

    Article  Google Scholar 

  • KUKA (2015) Hello Industrie 4.0: smart solutions for smart factories. http://www.kuka-robotics.com/res/sps/9cb8e311-bfd7-44b4-b0ea-b25ca883f6d3_KUKA_Industrie_4.0_EN.pdf. Accessed 3 Aug 2016

  • Levitin G, Rubinovitz J, Shnits B (2006) A genetic algorithm for robotic assembly line balancing. Eur J Oper Res 168(3):811–825

    Article  Google Scholar 

  • MacDougall W (2014) Industrie 4.0: smart manufacturing of the future. Berlin: Germany Trade & Invest. http://www.gtai.de/GTAI/Navigation/EN/Invest/Service/Publications/business-information,t=industrie-40--smart-manufacturing-for-the-future,did=917080.html. Accessed 3 Aug 2016

  • Moon DH, Cho HI, Kim HS, Sunwoo H, Jung JY (2006) A case study of the body shop design in an automotive factory using 3D simulation. Int J Prod Res 44(18–19):4121–4135

    Article  Google Scholar 

  • Mukund Nilakantan J, Ponnambalam SG (2016) Robotic U-shaped assembly line balancing using particle swarm optimization. Engineering Optimization 48(2):231–252

  • Mukund Nilakantan J, Huang GQ, Ponnambalam SG (2015) An investigation on minimizing cycle time and total energy consumption in robotic assembly line systems. J Clean Prod 90:311–325

    Article  Google Scholar 

  • Müller C, Spengler TS, Sodhi MS (2014) Robust flowline design for automotive body shops. In: Guan Y, Liao H (eds) Proceedings of the 2014 industrial and systems engineering research conference, pp 3077–3085

  • Müller C, Weckenborg C, Grunewald M, Spengler TS (2016) Consideration of redundancies in the configuration of automated flow lines. In: Mattfeld, Spengler, Brinkmann, Grunewald (eds) Logistics Management. Springer International Publishing, Cham

    Google Scholar 

  • Otto A, Otto C, Scholl A (2013) Systematic data generation and test design for solution algorithms on the example of SALBPGen for assembly line balancing. Eur J Oper Res 228(1):33–45

    Article  Google Scholar 

  • Rubinovitz J, Bukchin J (1991) Design and balancing of robotic assembly lines. In: Proceedings of the fourth world conference on robotics research, Pittsburgh, PA

  • Rubinovitz J, Bukchin J, Lenz E (1993) RALB—a heuristic algorithm for design and balancing of robotic assembly lines. CIRP Ann Manuf Technol 42(1):497–500

    Article  Google Scholar 

  • Salveson ME (1955) The assembly line balancing problem. J Ind Eng 6(3):18–25

    Google Scholar 

  • Scholl A, Becker C (2006) State-of-the-art exact and heuristic solution procedures for simple assembly line balancing. Eur J Oper Res 168(3):666–693

    Article  Google Scholar 

  • Spieckermann S, Gutenschwager K, Heinzel H, Voß S (2000) Simulation-based optimization in the automotive industry—a case study on body shop design. Simulation 75(5–6):276–286

    Google Scholar 

  • Tempelmeier H (2003) Practical considerations in the optimization of flow production systems. Int J Prod Res 41(1):149–170

    Article  Google Scholar 

  • Tempelmeier H, Schröer K, Schwarz M (2006) Simultane Optimierung der Taktzeiten und Puffergrössen im Karosseriebau. ZWF Zeitschrift für Wirtschaftlichen Fabrikbetrieb 101(1–2):35–41

    Article  Google Scholar 

  • Yoosefelahi A, Aminnayeri M, Mosadegh H, Ardakani HD (2012) Type II robotic assembly line balancing problem: an evolution strategies algorithm for a multi-objective model. J Manuf Syst 31(2):139–151

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Institute of Automotive Management and Industrial Production, Technische Universität Braunschweig, Mühlenpfordtstr. 23, 38118, Brunswick, Germany

    Christoph Müller, Martin Grunewald & Thomas S. Spengler

Authors
  1. Christoph Müller
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Martin Grunewald
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Thomas S. Spengler
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Christoph Müller.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Müller, C., Grunewald, M. & Spengler, T.S. Redundant configuration of automated flow lines based on “Industry 4.0”-technologies. J Bus Econ 87, 877–898 (2017). https://doi.org/10.1007/s11573-016-0831-7

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11573-016-0831-7

Keywords

JEL Classification

Search

Navigation