Flexible Services and Manufacturing Journal

, Volume 24, Issue 2, pp 142–170 | Cite as

Co-evolution of products and manufacturing capabilities and application in auto-parts assembly


Automotive engine accessories change frequently due to changes in design and market demands and their assembly lines also adapt to the new product requirements and new technologies. This paper presents novel co-evolution hypotheses and a model for products and their manufacturing capabilities, inspired by the biological co-evolution, which uses Cladistics analysis of the historical development data of both. The model embodies a three-fold approach; first it identifies evolution courses of products and manufacturing capabilities, then it searches for the best matching courses to ascertain manufacturing co-evolution, and finally informs future planning guided by the established co-evolution scheme. A case study of a class of automobile engine accessories and their corresponding assembly lines are modeled and used for illustration and validation. The results reveal the existence of strong symbiotic co-evolution relationships. The use of the proposed hypotheses and co-evolution model increases the efficiency of synthesizing and co-developing those accessories and their assembly lines in the future by highlighting their most evolved features and revealing the need for modifying product features and/or fully exploring the available manufacturing capabilities before acquiring new ones.


Manufacturing systems Co-evolution Cladistic Product design Automotive Assembly 


Taxa (taxon)

A product variant or a manufacturing system capability


Number of taxa in one group (products or manufacturing capabilities)


Number of characters (features) of one group

\( T_{P} \)

Cladogram (evolution hypothesis) of products

\( T_{M} \)

Cladogram (evolution hypothesis) of manufacturing capabilities

\( L(T) \)

Length of cladogram T (i.e. the number of emergent features of products or manufacturing capabilities through an evolution hypothesis)

\( T_{{P_{\min } }} \)

Most parsimonious product cladogram (minimum length)

\( T_{{M_{\min } }} \)

Most parsimonious manufacturing capabilities cladogram

\( d(T_{P} ,T_{M} ) \)

Cladistic difference between two cladograms

\( \Updelta_{t} d(T_{{P_{\min } }} ,T_{{M_{\min } }} ) \)

Cladistic difference between products and manufacturing capabilities over two generations at t and t + 1

\( d(T_{t + 1} ,T_{t} ) \)

Cladistic difference between one side cladograms (products or manufacturing capabilities) over two generations at t and t + 1


Index for x-axis location of a node in a cladogram topology


Index for x-axis location of a node in a cladogram topology


A reference for a specific taxon


A reference for a specific terminal


A reference for a specific character (feature)

\( \beta_{KIJ} \)

Cost of characters emergence through an evolution path (value 1 per occurrence)

\( X_{ij} \)

Evolutionary branching events representation through nodes connectivity to a cladogram topology

\( Y_{IJ} \)

A representation of taxa arrangement to cladogram topology terminals

\( C_{KI} \)

Accounting for the existence of a specific character in a specific taxon

\( \alpha_{Kij} \)

Accounting for nodes that do not hold a specific character

\( \gamma_{J} \)

Cost of nonconforming cladogram topologies for a specific terminal

\( \psi_{QJ} \)

Converting nodes connectivity to indices (upper matrix triangle)

\( \omega_{QJ} \)

Converting nodes connectivity to indices (lower matrix triangle)

\( \lambda_{J} \)

Index for x-axis location of the node that connects a specific terminal to a cladogram topology

\( \mu_{J} \)

Index for y-axis location of the node that connects a specific terminal to a cladogram topology

\( \eta_{J} \)

Topologies nonconformity detection (calculates indices difference of counterpart connecting nodes)

\( \nu_{J} \)

Nonconformity values normalization (converts positive differences into a 1 unit cost)


  1. Abdi MR (2009) Layout configuration selection for reconfigurable manufacturing systems using the fuzzy ahp. Int J Manuf Technol Manage 17(1-2):149–165MathSciNetGoogle Scholar
  2. Abdi, Labib AW (2004) Grouping and selecting products: the design key of reconfigurable manufacturing systems (rmss). Int J Prod Res 42(3):521–546CrossRefGoogle Scholar
  3. AlGeddawy T, ElMaraghy H (2009) Management of co-evolution in manufacturing systems. 3rd international conference on changeable, agile, reconfigurable and virtual production (CARV 2009). Munich, GermanyGoogle Scholar
  4. AlGeddawy T, ElMaraghy H (2010) A model for co-evolution in manufacturing based on biological analogy. Int J Prod Res, In printGoogle Scholar
  5. Buchin K, Buchin M, Byrka J, Nollenburg M, Okamoto Y et al (2009) Drawing (complete) binary tanglegrams hardness, approximation, fixed-parameter tractability. Springer Verlag, Heraklion, Crete, Greece, pp 324–335Google Scholar
  6. Cassidy RL, Fan SK, MacDonald RS, Samson WF (1979) Serpentine—extended life accessory drive. SAE Preprints, (790699)Google Scholar
  7. ElMaraghy H (2005) Flexible and reconfigurable manufacturing systems paradigms. Int J Flexible Manuf Syst 17(4):261–276CrossRefGoogle Scholar
  8. ElMaraghy HA (2007) Reconfigurable process plans for responsive manufacturing systems. In: Cunha F, Maropoulos G (eds) Digital enterprise technology: perspectives and future challenges. Springer Science, NY, pp 35–44Google Scholar
  9. ElMaraghy H, AlGeddawy T, Azab A (2008) Modelling evolution in manufacturing: a biological analogy. CIRP Ann Manuf Tech 57(1):467–472CrossRefGoogle Scholar
  10. ElMaraghy H, Azab A, Schuh G, Pulz C (2009) Managing variations in products, processes and manufacturing systems. CIRP Ann Manuf Tech 58(1):441–446CrossRefGoogle Scholar
  11. Frei R, Di Marzo Serugendo G, Barata J (2008) Designing self-organization for evolvable assembly systemsed. Second IEEE international conference on self-adaptive and self-organizing systems (SASO), Piscataway, NJ, USA: IEEE, pp 97–106Google Scholar
  12. Fujimoto T (1999) The evolution of a manufacturing system at Toyota. Oxford University Press, New YorkGoogle Scholar
  13. Groover M (2001) Automation, production systems, and computer-integrated manufacturing, 2nd edn. Printice Hall, NJGoogle Scholar
  14. Hennig W (1966) Phylogenitic systematics. University of Illinois Press, UrbanaGoogle Scholar
  15. Huang G, Bin S, Halevi G (2003) Product platform identification and development for mass customization. CIRP Ann Manuf Tech 52(1):117–120CrossRefGoogle Scholar
  16. Koren Y (2002) Reconfigurable manufacturing systems. CIRP 1st international conference on agile and reconfigurable manufacturing. Ann ArborGoogle Scholar
  17. Kuzgunkaya O, ElMaraghy HA (2006) Assessing the structural complexity of manufacturing systems configurations. Int J Flexible Manuf Syst 18(2):145–171. doi: 10.1007/s10696-006-9012-2 MATHCrossRefGoogle Scholar
  18. Leon C (1964) Heuristic methods for location-allocation problems. SIAM Review 6(1):37–53MathSciNetCrossRefGoogle Scholar
  19. Lozano A, Pinter R, Rokhlenko O, Valiente G, Ziv-Ukelson M (2007) Seeded tree alignment and planar tanglegram layout. Algor Bioinformatics 4645:98–110CrossRefGoogle Scholar
  20. Monostori L, Váncza J, Kumara S (2006) Agent-based systems for manufacturing. CIRP Annals 22(2):697–720CrossRefGoogle Scholar
  21. Page RDM (1994) Parallel phylogenies: reconstructing the history of host-parasite assemblages. Cladistics 10(2):155–173CrossRefGoogle Scholar
  22. Page R (1998) Genetree: comparing gene and species phylogenies using reconciled trees. Bioinformatics 14(9):819–820. doi: 10.1093/bioinformatics/14.9.819 CrossRefGoogle Scholar
  23. Page RDM (2003) Tangled trees: phylogeny, cospeciation, and coevolution. University of Chicago Press, ChicagoGoogle Scholar
  24. Rajagopalan S (1993) Flexible versus dedicated technology: a capacity expansion model. Int J Flexible Manuf Syst 5(Copyright 1993, IEE):129–142Google Scholar
  25. Ridely M (2004) Evolution, 3rd edn. Blackwell Publishing, USAGoogle Scholar
  26. Shabaka AI, ElMaraghy HA (2007) Generation of machine configurations based on product features. Int J Comput Integrated Manuf 20(4):355–369CrossRefGoogle Scholar
  27. Shennan S (2009) Pattern and process in cultural evolution Berkeley. University of California Press, USAGoogle Scholar
  28. Simpson TW (2004) Product platform design and customization: status and promise. Artificial intelligence for engineering design, analysis and manufacturing: AIEDAM 18(1):3–20Google Scholar
  29. Tolio T, Valente A (2009) A stochastic programming approach to design the production system flexibility considering the evolution of the part families. Int J Manuf Tech Manage 17(1–2):42–67Google Scholar
  30. Ueda K, Kito T, Fujii N (2006) Modeling biological manufacturing systems with bounded-rational agents. CIRP Ann Manuf Tech 55(1):469–472CrossRefGoogle Scholar
  31. Ulsoy AG, Whitesell JE, Hooven MD (1985) Design of belt-tensioner systems for dynamic stabilized. Cincinnati, OH, Engl: ASME, ASME, NY, USAGoogle Scholar
  32. Valckenaers P, Van Brussel H (2005) Holonic manufacturing execution systems. CIRP Ann Manuf Tech 54(1):427–432CrossRefGoogle Scholar
  33. Wake WK (2000) Design paradigms: a sourcebook for creative visualization. Wiley, New YorkGoogle Scholar
  34. Wiendahl H-P, ElMaraghy HA, Nyhuis P, Zaeh M, Wiendahl H-H et al (2007) Changeable manufacturing: classification, design, operation. CIRP Annals 56(2):783–809CrossRefGoogle Scholar
  35. Youssef AM, ElMaraghy HA (2007) Optimal configuration selection for reconfigurable manufacturing systems. Int J Flexible Manuf Syst 19(2):67–106MATHCrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2011

Authors and Affiliations

  1. 1.Intelligent Manufacturing Systems (IMS) Centre, Faculty of EngineeringUniversity of WindsorWindsorCanada

Personalised recommendations