Journal of Business Economics

, Volume 87, Issue 7, pp 877–898 | Cite as

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

  • Christoph MüllerEmail author
  • Martin Grunewald
  • Thomas S. Spengler
Original Paper


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.


Industry 4.0 Flow line Line balancing Line configuration Redundancy Robotics 

JEL Classification



  1. 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):1005–1016CrossRefGoogle Scholar
  2. Battaïa O, Dolgui A (2013) A taxonomy of line balancing problems and their solution approaches. Int J Prod Econ 142(2):259–277CrossRefGoogle Scholar
  3. Baybars I (1986) A survey of exact algorithms for the simple assembly line balancing problem. Manage Sci 32(8):909–932CrossRefGoogle Scholar
  4. Becker C, Scholl A (2006) A survey on problems and methods in generalized assembly line balancing. Eur J Oper Res 168(3):694–715CrossRefGoogle Scholar
  5. Belmansour A-T, Nourelfath M (2012) Performance evaluation of a reconfigurable production line. Int J Perform Eng 8(4):427–435Google Scholar
  6. Boysen N, Fliedner M (2006) Ein flexibler zweistufiger Graphen-Algorithmus zur Fließbandabstimmung mit praxisrelevanten Nebenbedingungen. Zeitschrift für Betriebswirtschaft 76(1):55–78CrossRefGoogle Scholar
  7. Boysen N, Fliedner M, Scholl A (2007a) A classification of assembly line balancing problems. Eur J Oper Res 183(2):674–693CrossRefGoogle Scholar
  8. Boysen N, Fliedner M, Scholl A (2007b) Produktionsplanung bei Variantenfließfertigung: Planungshierarchie und Elemente einer Hierarchischen Planung. Zeitschrift für Betriebswirtschaft 77(7–8):759–793CrossRefGoogle Scholar
  9. Boysen N, Fliedner M, Scholl A (2008) Assembly line balancing: which model to use when? Int J Prod Econ 111(2):509–528CrossRefGoogle Scholar
  10. Brynjolfsson E, McAfee A (2014) The second machine age. Work, progress, and prosperity in a time of brilliant technologies. Norton, New YorkGoogle Scholar
  11. Bukchin J, Rubinovitz J (2003) A weighted approach for assembly line design with station paralleling and equipment selection. IIE Trans 35(1):73–85CrossRefGoogle Scholar
  12. Bukchin J, Tzur M (2000) Design of flexible assembly line to minimize equipment cost. IIE Trans 32(7):585–598Google Scholar
  13. Conway R, Maxwell W, McClain JO, Thomas LJ (1988) Role of work-in-process inventory in serial production lines. Oper Res 36(2):229–241CrossRefGoogle Scholar
  14. Daoud S, Chehade H, Yalaoui F, Amodeo L (2014) Solving a robotic assembly line balancing problem using efficient hybrid methods. J Heuristics 20(3):235–259CrossRefGoogle Scholar
  15. Dar-El EM (1973) MALB—a heuristic technique for balancing large single-model assembly lines. AIIE Trans 5(4):343–356CrossRefGoogle Scholar
  16. Deloitte (2015) Industry 4.0: Challenges and solutions for the digital transformation and use of exponential technologies. Accessed 3 Aug 2016
  17. Evans PC, Annuziata M (2012) Industrial Internet: pushing the Boundaries of minds and machines. Accessed 3 Aug 2016
  18. 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):1065–1080CrossRefGoogle Scholar
  19. Graves SC, Lamar BW (1983) Integer programming procedure for assembly system design problems. Oper Res 31(3):522–545CrossRefGoogle Scholar
  20. Graves SC, Redfield CH (1988) Equipment selection and task assignment for multiproduct assembly system design. Int J Flex Manuf Syst 1(1):31–50CrossRefGoogle Scholar
  21. 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, HauppaugeGoogle Scholar
  22. Helber S (2000) Kapitalwertorientierte Pufferallokation in stochastischen Fließproduktionssystemen. Schmalenbachs Zeitschrift für betriebswirtschaftliche Forschung: Zfbf 52(3):211–233Google Scholar
  23. Helber S (2001) Cash-flow-oriented buffer allocation in stochastic flow lines. Int J Prod Res 39(14):3061–3083CrossRefGoogle Scholar
  24. Inman RR (1999) Empirical evaluation of exponential and independence assumptions in queueing models of manufacturing systems. Prod Oper Manag 8(4):409–432CrossRefGoogle Scholar
  25. International Federation of Robotics (2015) Industrial Robots Statistics. Accessed 3 Aug 2016
  26. Kagermann H, Wahlster W, Helbig J (2013) Umsetzungsempfehlungen für das Zukunftsprojekt Industrie 4.0. Abschlussbericht des Arbeitskreises Industrie 4.0, Frankfurt/MainGoogle Scholar
  27. 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):315–326CrossRefGoogle Scholar
  28. Kim H, Park S (1995) Strong cutting plane algorithm for the robotic assembly line balancing problem. Int J Prod Res 33(8):2311–2323CrossRefGoogle Scholar
  29. KUKA (2015) Hello Industrie 4.0: smart solutions for smart factories. Accessed 3 Aug 2016
  30. Levitin G, Rubinovitz J, Shnits B (2006) A genetic algorithm for robotic assembly line balancing. Eur J Oper Res 168(3):811–825CrossRefGoogle Scholar
  31. MacDougall W (2014) Industrie 4.0: smart manufacturing of the future. Berlin: Germany Trade & Invest.,t=industrie-40--smart-manufacturing-for-the-future,did=917080.html. Accessed 3 Aug 2016
  32. 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):4121–4135CrossRefGoogle Scholar
  33. Mukund Nilakantan J, Ponnambalam SG (2016) Robotic U-shaped assembly line balancing using particle swarm optimization. Engineering Optimization 48(2):231–252Google Scholar
  34. 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 90:311–325CrossRefGoogle Scholar
  35. 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 3077–3085Google Scholar
  36. 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, ChamGoogle Scholar
  37. 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):33–45CrossRefGoogle Scholar
  38. Rubinovitz J, Bukchin J (1991) Design and balancing of robotic assembly lines. In: Proceedings of the fourth world conference on robotics research, Pittsburgh, PAGoogle Scholar
  39. 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):497–500CrossRefGoogle Scholar
  40. Salveson ME (1955) The assembly line balancing problem. J Ind Eng 6(3):18–25Google Scholar
  41. 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):666–693CrossRefGoogle Scholar
  42. 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):276–286Google Scholar
  43. Tempelmeier H (2003) Practical considerations in the optimization of flow production systems. Int J Prod Res 41(1):149–170CrossRefGoogle Scholar
  44. Tempelmeier H, Schröer K, Schwarz M (2006) Simultane Optimierung der Taktzeiten und Puffergrössen im Karosseriebau. ZWF Zeitschrift für Wirtschaftlichen Fabrikbetrieb 101(1–2):35–41CrossRefGoogle Scholar
  45. 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):139–151CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  • Christoph Müller
    • 1
    Email author
  • Martin Grunewald
    • 1
  • Thomas S. Spengler
    • 1
  1. 1.Institute of Automotive Management and Industrial ProductionTechnische Universität BraunschweigBrunswickGermany

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