Reconfigurability in cellular manufacturing systems: a design model and multi-scenario analysis

  • Marco BortoliniEmail author
  • Francesco Gabriele Galizia
  • Cristina Mora
  • Francesco Pilati


Within cellular manufacturing systems (CMSs), families of parts are assigned to manufacturing cells, composed by homogeneous sets of machines. In conventional CMSs, each cell is devoted to the production of a specific part family, reducing material handling and work-in-process. Despite their flexibility, such systems still suffer from coping with the present market challenges asking for dynamic part mix and the need of agility in manufacturing. To meet these challenges, the recent literature explores the idea of including elements of the emerging reconfigurable manufacturing paradigm in the design and management of CMSs, leading to the cellular reconfigurable manufacturing system (CRMS) concept. The aim of this paper is to propose an original linear programming optimization model for the design of CRMSs with alternative part routing and multiple time periods. The production environment consists of multiple cells equipped with reconfigurable machine tools (RMTs) made of basic and auxiliary custom modules. By changing the auxiliary modules, different operations become available on the same RMT. The proposed approach determines the part routing mix and the auxiliary module allocation best balancing the part flows among RMTs and the effort to install the modules on the machines. The approach discussion is supported by a literature case study, while a multi-scenario analysis is performed to assess the impact of different CMS configurations on the system performances, varying both the number of cells and the RMT assignment to each of them. A benchmarking concludes the paper comparing the proposed CRMS against a conventional CMS configuration. The analysis shows relevant benefits in terms of reduction of the intercellular travel time (− 58.6%) getting a global time saving of about 53.3%. Results prove that reconfigurability is an opportunity for industries to face the dynamics of global markets.


Cellular manufacturing Reconfigurable manufacturing systems Reconfigurability Modularity Optimization 


Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.


  1. 1.
    Singh N (1993) Digital of cellular manufacturing systems: an invited review. Eur J Oper Res 69(3):284–291CrossRefGoogle Scholar
  2. 2.
    Wemmerlov U, Johnson DJ (1997) Cellular manufacturing at 46 user plants: implementation experiences and performance improvements. Int J Prod Res 35(1):29–49CrossRefGoogle Scholar
  3. 3.
    Defersha FM, Chen M (2005) A comprehensive mathematical model for the design of cellular manufacturing systems. Int J Prod Econ 103:767–783CrossRefGoogle Scholar
  4. 4.
    Sarker BR (2001) Measures of grouping efficiency in cellular manufacturing systems. Eur J Oper Res 130:588–611CrossRefGoogle Scholar
  5. 5.
    Chan FTS, Lau KW, Chan LY, Lo VHY (2008) Cell formation problem with consideration of both intracellular and intercellular movements. Int J Prod Res 46(19):2589–2620CrossRefGoogle Scholar
  6. 6.
    Bortolini M, Faccio M, Gamberi M, Manzini R, Pilati F (2016) Stochastic timed Petri nets to dynamically design and simulate industrial production processes. Int J Logistics Syst Manag 25(1):20–43CrossRefGoogle Scholar
  7. 7.
    Lolli F, Gamberini R, Gamberi M, Bortolini M (2018) The training of suppliers: a linear model for optimising the allocation of available hours. Int J Ind Syst Eng 28(2):135–151Google Scholar
  8. 8.
    Manzini R, Accorsi R, Bortolini M (2013) Similarity-based cluster analysis for the cell formation problem. In Industrial Engineering: Concepts, Methodologies, Tools, and Applications, IGI Global, pp 499-521Google Scholar
  9. 9.
    Mehrabi MG, Ulsoy AG, Koren Y (2000) Reconfigurable manufacturing systems: key to future manufacturing. J Intell Manuf 11(4):403–419CrossRefGoogle Scholar
  10. 10.
    Mehrabi MG, Ulsoy AG, Koren Y, Heytler P (2002) Trends and perspectives in flexible and reconfigurable manufacturing systems. J Intell Manuf 13(2):135–146CrossRefGoogle Scholar
  11. 11.
    Molina A, Rodriguez CA, Ahuett H, Cortés JA, Ramirez M, Jimenez G, Martinez S (2005) Next-generation manufacturing systems: key research issues in developing and integrating reconfigurable and intelligent machines. Int J Comput Integr Manuf 18(7):525–536CrossRefGoogle Scholar
  12. 12.
    Hasan F, Jain PK, Kumar D (2014) Service level as performance index for reconfigurable manufacturing system involving multiple part families. Procedia Eng 69:814–821CrossRefGoogle Scholar
  13. 13.
    Bortolini M, Galizia FG, Mora C (2018) Reconfigurable manufacturing systems: literature review and research trend. J Manuf Syst 49:93–106CrossRefGoogle Scholar
  14. 14.
    Pattanaik LN, Jain PK, Mehta NK (2007) Cell formation in the presence of reconfigurable machines. Int J Adv Manuf Technol 34:335–345CrossRefGoogle Scholar
  15. 15.
    Eguia I, Molina JC, Lozano S, Racero J (2017) Cell design and multi-period machine loading in cellular reconfigurable manufacturing systems with alternative routing. Int J Prod Res 55(10):2775–2790CrossRefGoogle Scholar
  16. 16.
    Koren Y, Heisel U, Jovane F, Moriwaki T, Pritschow G, Ulsoy G, Van Brussel H (1999) Reconfigurable manufacturing systems. CIRP Ann Manuf Technol 48(2):527–540CrossRefGoogle Scholar
  17. 17.
    Koren Y (2006) General RMS characteristics. Comparison with dedicated and flexible systems. In: Reconfigurable manufacturing systems and transformable factories. Springer, Berlin Heidelberg, pp 27–45CrossRefGoogle Scholar
  18. 18.
    Landers RG, Min BK, Koren Y (2001) Reconfigurable machine tools. CIRP Ann Manuf Technol 50:269–274CrossRefGoogle Scholar
  19. 19.
    Moghaddam SK, Houshmand M, Fatahi Valilai O (2018) Configuration design in scalable reconfigurable manufacturing systems (RMS); a case of single-product flow line (SPFL). Int J Prod Res 56(11):3932–3954CrossRefGoogle Scholar
  20. 20.
    Asghar E, uz Zaman UK, Baqai AA, Homri L (2018) Optimum machine capabilities for reconfigurable manufacturing systems. Int J Adv Manuf Technol 95(9-12):4397–4417CrossRefGoogle Scholar
  21. 21.
    Benderbal HH, Benyoucef L (2019) Machine layout design problem under product family evolution in reconfigurable manufacturing environment: a two-phase-based AMOSA approach. Int J Adv Manuf Technol:1–15Google Scholar
  22. 22.
    Xing B, Nelwamondo FV, Gao W, Marwala T (2009) Application of artificial intelligence (AI) methods for designing and analysis of reconfigurable cellular manufacturing systems (RCMS). Proceedings of the 2nd International Conference on Adaptive Science & Technology, pp 402-409Google Scholar
  23. 23.
    Pattanaik IN, Sharma BP (2009) Implementing lean manufacturing with cellular layout: a case study. Int J Adv Manuf Technol 42:772–779CrossRefGoogle Scholar
  24. 24.
    Bortolini M, Manzini R, Accorsi R, Mora C (2011) A hybrid procedure for machine duplication in cellular manufacturing systems. Int J Adv Manuf Technol 57:1155–1173CrossRefGoogle Scholar
  25. 25.
    Durmusoglu MB, Cevikcan E, Satoglu SI (2018) The progress of assembly cell design from a conventional assembly system to a walking worker assembly cell. In: Süer GA, Gen M (eds) Cellular manufacturing systems: recent developments, analysis and case studies. Nova Science Publishers, Inc., New York, pp 327–343 ISBN: 978-153612880-2Google Scholar
  26. 26.
    Yilmaz ÖF, Durmuşoğlu MB (2018) Evolutionary algorithms for multi-objective scheduling in a hybrid manufacturing system. In: Handbook of research on applied optimization methodologies in manufacturing systems. IGI Global Publications, Hershey, PA, pp 162–187CrossRefGoogle Scholar
  27. 27.
    Ateme-Nguema BH, Dao TM (2007) Optimization of cellular manufacturing systems design using the hybrid approach based on the ant colony and tabu search techniques. Proceedings of the IEEE International Conference on Industrial Engineering and Engineering Management, pp 668-673Google Scholar
  28. 28.
    Luo J, Tang L (2009) A hybrid approach of ordinal optimization and iterated local search for manufacturing cell formation. Int J Adv Manuf Technol 40:362–372CrossRefGoogle Scholar
  29. 29.
    Ghezavati V, Saidi-Mehrabad M (2010) Designing integrated cellular manufacturing systems with scheduling considering stochastic processing time. Int J Adv Manuf Technol 48:701–717CrossRefGoogle Scholar
  30. 30.
    Yilmaz OF, Cevikcan E, Durmusoglu MB (2016) Scheduling batches in multi hybrid cell manufacturing system considering worker resources: a case study from pipeline industry. Adv Prod Eng Manag 11(3):192–206Google Scholar
  31. 31.
    Yilmaz OF, Durmusoglu MB (2018) A performance comparison and evaluation of metaheuristics for a batch scheduling problem in a multi-hybrid cell manufacturing system with skilled workforce assignment. J Ind Manag Optim 14(3):1219–1249MathSciNetzbMATHGoogle Scholar
  32. 32.
    Pattanaik LN, Kumar V (2010) Multiple level of reconfiguration for robust cells formed using modular machines. Int J Ind Syst Eng 5:424–441Google Scholar
  33. 33.
    Bai JJ, Gong YG, Wang NS, Tang DB (2009) Methodology of virtual manufacturing cell formation in reconfigurable manufacturing system for make-to-order manufacturing. Comput Integr Manuf Syst 2:016Google Scholar
  34. 34.
    Javadian N, Aghajani A, Rezaeian J, Sebdani MJG (2011) A multi-objective integrated cellular manufacturing systems design with dynamic system reconfiguration. Int J Adv Manuf Technol 56(1-4):307–317CrossRefGoogle Scholar
  35. 35.
    Ossama M, Youssef AMA, Shalaby MA (2014) A multi-period cell formation model for reconfigurable manufacturing systems. Procedia CIRP 17:130–135CrossRefGoogle Scholar
  36. 36.
    Eguia I, Lozano S, Racero J, Guerrero F (2013) Cell design and loading with alternative routing in cellular reconfigurable manufacturing systems. IFAC Proc Volumes 46(9):1744–1749CrossRefGoogle Scholar
  37. 37.
    Eguia I, Racero J, Guerrero F, Lozano S (2013) Cell formation and scheduling of part families for reconfigurable cellular manufacturing systems using tabu search. Simulation 89:1056–1072CrossRefGoogle Scholar
  38. 38.
    Yu JM, Doh HH, Kim HW, Kin JS, Lee DH, Nam SH (2012) Iterative algorithms for part grouping and loading in cellular reconfigurable manufacturing systems. J Oper Res Soc 63:1635–1644CrossRefGoogle Scholar
  39. 39.
    Aljuneidi T, Bulgak AA (2016) A mathematical model for designing reconfigurable cellular hybrid manufacturing-remanufacturing systems. Int J Adv Manuf Technol 87(5-8):1585–1596CrossRefGoogle Scholar
  40. 40.
    King JR (1980) Machine-component grouping in production flow analysis: an approach using a rank order clustering algorithm. Int J Prod Res 18(2):213–232MathSciNetCrossRefGoogle Scholar
  41. 41.
    Gupta T, Seifoddini H (1990) Production data based similarity coefficient for machine-component grouping decisions in the design of a cellular manufacturing system. Int J Prod Res 28(7):1247–1269CrossRefGoogle Scholar
  42. 42.
    Rennie BC, Dobson AJ (1969) On Stirling numbers of the second kind. J Combin Theory 7(2):116–121MathSciNetCrossRefGoogle Scholar

Copyright information

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

Authors and Affiliations

  1. 1.Department of Industrial EngineeringUniversity of BolognaBolognaItaly
  2. 2.Department of Management and EngineeringUniversity of PadovaVicenzaItaly

Personalised recommendations