Journal of Intelligent Manufacturing

, Volume 28, Issue 2, pp 353–369 | Cite as

Measures of reconfigurability and its key characteristics in intelligent manufacturing systems

  • Amro M. FaridEmail author


In recent years, the fields of reconfigurable manufacturing systems, holonic manufacturing systems, and multi-agent systems have made technological advances to support the ready reconfiguration of automated manufacturing systems. While these technological advances have demonstrated robust operation and been qualitatively successful in achieving reconfigurability, limited effort has been devoted to the measurement of reconfigurability in the resultant systems. Hence, it is not clear (1) to which degree these designs have achieved their intended level of reconfigurability, (2) which systems are indeed quantitatively more reconfigurable and (3) how these designs may overcome their design limitations to achieve greater reconfigurability in subsequent design iterations. Recently, a reconfigurability measurement process based upon axiomatic design knowledge base and the design structure matrix has been developed. Together, they provide quantitative measures of reconfiguration potential and ease. This paper now builds upon these works to provide a set of composite reconfigurability measures. Among these are measures for the key characteristics of reconfigurability: integrability, convertibility, and customization, which have driven the qualitative and intuitive design of these technological advances. These measures are then demonstrated on an illustrative example followed by a discussion of how they adhere to requirements for reconfigurability measurement in automated and intelligent manufacturing systems.


Reconfigurability Axiomatic design for large flexible systems Design structure matrix Reconfigurable manufacturing systems Multi-agent systems Holonic manufacturing systems 


  1. Abadir, K. M., & Magnus, J. R. (2005). Matrix algebra (Vol. 1). Cambridge: Cambridge University Press.CrossRefGoogle Scholar
  2. ANSI-ISA. (2005). Enterprise control system integration part 3: Activity models of manufacturing operations management. Technical report: The International Society of Automation.Google Scholar
  3. Babiceanu, R., & Chen, F. (2006). Development and applications of holonic manufacturing systems: A survey. Journal of Intelligent Manufacturing, 17, 111–131.CrossRefGoogle Scholar
  4. Baca, E. E. S., Farid, A. M., & Tsai, I. T. (2013) An axiomatic design approach to passenger itinerary enumeration in reconfigurable transportation systems. In Proceedings of ICAD2013 the 7th international conference on axiomatic design, Worcester, MA (pp. 138–145).
  5. Bellifemine, F., Caire, G., & Greenwood, D. (2007) Developing Multi-agent systems with JADE. Wiley, Hoboken.
  6. Brennan, R., & Norrie, D. H. (2001). Agents, holons and function blocks: Distributed intelligent control in manufacturing. Journal of Applied Systems Science: Special Issue, 2(1), 1–19.Google Scholar
  7. Carr, J. (2002) Recipes for intelligent products. Technical Report.Google Scholar
  8. Cerni, R. H., & Foster, L. E. (1962). Instrumentation for engineering measurment. Hoboken: Wiley.Google Scholar
  9. Dashchenko, A. I. (2006). Reconfigurable manufacturing systems and transformable factories. Berlin: Springer.CrossRefGoogle Scholar
  10. Dijkstra, E. W. (1990). Beyond structured programming: An introduction to the principles of software metrics. Journal of Structured Programming, 11(1) 268–277.Google Scholar
  11. Ejiogu, L. O. (1991). Software engineering with formal metrics. New York: QED Technical Publishing Group: McGraw-Hill Book Company.Google Scholar
  12. Farid, A. M. (2007). Reconfigurability measurement in automated manufacturing systems. Ph.d. dissertation, University of Cambridge Engineering Department Institute for Manufacturing.
  13. Farid, A. M. (2008a). Facilitating ease of system reconfiguration through measures of manufacturing modularity. In Proceedings of the Institution of Mechanical Engineers, Part B (Journal of Engineering Manufacture)-invited paper, 222(B10):1275–1288. doi: 10.1243/09544054JEM1055.
  14. Farid, A. M. (2008b). Product degrees of freedom as manufacturing system reconfiguration potential measures. International Transactions on Systems Science and Applications-invited paper, 4(3):227–242.
  15. Farid, A. M. (2013). An axiomatic design approach to non-assembled production path enumeration in reconfigurable manufacturing systems. In 2013 IEEE international conference on systems man and cybernetics (pp. 1–8). Manchester, UK. doi: 10.1109/SMC.2013.659.
  16. Farid, A. M. (2014a). Static resilience of large flexible engineering systems : part I—Axiomatic design model. In 4th International engineering systems symposium (pp. 1–8). Hoboken, NJ.
  17. Farid, A. M. (2014b) Static resilience of large flexible engineering systems : Part II—axiomatic design measures. In 4th International engineering systems symposium (pp. 1–8)Google Scholar
  18. Farid, A. M., & Covanich, W. (2008). Measuring the effort of a reconfiguration process. In IEEE international conference on emerging technologies and factory automation, 2008. ETFA 2008 (pp. 1137–1144). Hamburg, Germany. doi: 10.1109/ETFA.2008.4638540.
  19. Farid, A. M., & McFarlane, D. C. (2006a). A development of degrees of freedom for manufacturing systems. In IMS’2006: 5th international symposium on intelligent manufacturing systems: agents and virtual worlds (pp. 1–6). Sakarya, Turkey.
  20. Farid, A. M., & McFarlane, D. C. (2006b). A tool for assessing reconfigurability of distributed manufacturing systems. In: 12th IFAC symposium on information control problems in manufacturing, INCOM 2006, and associated industrial meetings: EMM’2006, BPM’2006, JT’2006, May 17, 2006– May 19, 2006, IFAC Secretariat, Saint Etienne, France, IFAC Proceedings Volumes (IFAC-PapersOnline) (Vol. 12, pp. 1–6).
  21. Farid, A. M., & McFarlane, D. C. (2006c). An approach to the application of the design structure matrix for assessing reconfigurability of distributed manufacturing systems. In Proceedings of the IEEE workshop on distributed intelligent systems: Collective intelligence and its applications (DIS’06), IEEE Comput. Soc, Prague, Czech republic, IEEE workshop on distributed intelligent systems: collective intelligence and its applications (pp. 1–6). doi: 10.1109/DIS.2006.10.
  22. Farid, A. M., & McFarlane, D. C. (2007). A design structure matrix based method for reconfigurability measurement of distributed manufacturing systems. International Journal of Intelligent Control and Systems Special Issue- invited paper, 12(2):118–129.
  23. Farid, A. M., & McFarlane, D. C. (2008). Production degrees of freedom as manufacturing system reconfiguration potential measures. Proceedings of the Institution of Mechanical Engineers, Part B (Journal of Engineering Manufacture)-invited paper, 222(B10):1301–1314. doi: 10.1243/09544054JEM1056,
  24. Friedenthal, S., Moore, A., & Steiner, R. (2011). A practical guide to SysML: The systems modeling language (2nd ed.). Burlington, MA: Morgan Kaufmann.Google Scholar
  25. Gasevic, D., Djuric, D., & Devedzic, V. (2009). Model driven engineering and ontology development (2nd ed.). Dordrecht: Springer.Google Scholar
  26. Group, O. (2007). Common object request broker architecture: Core specification. Technical report.Google Scholar
  27. Heilala, J., & Voho, P. (2001). Modular reconfigurable flexible final assembly systems. Assembly Automation, 21(1), 20–28.CrossRefGoogle Scholar
  28. Hirtz, J., Stone, R., McAdams, D., Szykman, S., & Wood, K. (2002). A functional basis for engineering design: Reconciling and evolving previous efforts. Research in Engineering Design, 13, 65–82.CrossRefGoogle Scholar
  29. Koren, Y., Heisel, U., Jovane, F., Moriwaki, T., Pritschow, G., Ulsoy, G., et al. (1999). Reconfigurable manufacturing systems. CIRP Annals: Manufacturing Technology, 48(2), 527–540.CrossRefGoogle Scholar
  30. Kristianto, Y., Helo, P., & Jiao, R. (2013). Mass customization design of engineer-to-order products using Benders’ decomposition and bi-level stochastic programming. Journal of Intelligent Manufacturing, 24(5), 961–975. doi: 10.1007/s10845-012-0692-z.CrossRefGoogle Scholar
  31. Landers, R. G., Min, B. K., & Koren, Y. (2001). Reconfigurable machine tools. CIRP Annals: Manufacturing Technology, 50, 269–274.CrossRefGoogle Scholar
  32. Leitao, P., & Restivo, F. (2006). ADACOR: A holonic architecture for agile and adaptive manufacturing control. Computers in Industry, 57(2), 121–130. doi: 10.1016/j.compind.2005.05.005.CrossRefGoogle Scholar
  33. Leitao, P. (2009). Agent-based distributed manufacturing control: A state-of-the-art survey. Engineering Applications of Artificial Intelligence, 22(7), 979–991. doi: 10.1016/j.engappai.2008.09.005.CrossRefGoogle Scholar
  34. Leitao, P., Barbosa, J., & Trentesaux, D. (2012). Bio-inspired multi-agent systems for reconfigurable manufacturing systems. Engineering Applications of Artificial Intelligence, 25(5), 934–944. doi: 10.1016/j.engappai.2011.09.025.CrossRefGoogle Scholar
  35. Lepuschitz, W., Zoitl, A., Vallee, M., & Merdan, M. (2010). Toward self-reconfiguration of manufacturing systems using automation agents. IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews, 41(1), 52–69. doi: 10.1109/TSMCC.2010.2059012.CrossRefGoogle Scholar
  36. Lin, Y. I., Chou, Y. W., Shiau, J. Y., & Chu, C. H. (2013). Multi-agent negotiation based on price schedules algorithm for distributed collaborative design. Journal of Intelligent Manufacturing, 24(3), 545–557. doi: 10.1007/s10845-011-0609-2.CrossRefGoogle Scholar
  37. Marik, V., Fletcher, M., Pechoucek, M., Stepankova, O., Krautwurmova, H., & Luck, M. (2002) Holons and agents: Recent developments and mutual impacts. In Multi-agent systems and applications II: Lecture Notes in Artificial Intelligence (pp. 233–267). Springer, Berlin.Google Scholar
  38. Marik, V., & McFarlane, D. (2005). Industrial adoption of agent-based technologies. IEEE Intelligent Systems [see also IEEE Intelligent Systems and Their Applications], 20(1), 27–35.CrossRefGoogle Scholar
  39. McFarlane, D., & Bussmann, S. (2000). Developments in holonic production planning and control. Production Planning and Control, 11(6), 522–536.CrossRefGoogle Scholar
  40. McFarlane, D., Bussmann, S., & Deen, S. M. (2003). Holonic manufacturing control: Rationales, developments and open issues. In: Agent-Based Manufacturing (pp. 303–326). Berlin: Springer.Google Scholar
  41. McFarlane, D., Carr, C., Harrison, M., & McDonald, A. (2002). Auto-ID’s three R’s: Rules and recipes for product requirements. Technical report.Google Scholar
  42. Mehrabi, M. G., Ulsoy, A. G., & Koren, Y. (2000a). Reconfigurable manufacturing systems and their enabling technologies. International Journal of Manufacturing Technology and Management, 1(1), 113–130.CrossRefGoogle Scholar
  43. Mehrabi, M. G., Ulsoy, A. G., & Koren, Y. (2000b). Reconfigurable manufacturing systems: Key to future manufacturing. Journal of Intelligent Manufacturing, 11(4), 403–419.CrossRefGoogle Scholar
  44. Mehrabi, M. G., Ulsoy, A. G., Koren, Y., & Heytler, P. (2002). Trends and perspectives in flexible and reconfigurable manufacturing systems. Journal of Intelligent Manufacturing, 13(2), 135–146.CrossRefGoogle Scholar
  45. Müller, R., Esser, M., & Vette, M. (2013). Reconfigurable handling systems as an enabler for large components in mass customized production. Journal of Intelligent Manufacturing, 24(5), 977–990. doi: 10.1007/s10845-012-0624-y.CrossRefGoogle Scholar
  46. Munroe, M. E. (1971). Measure and integration. Reading, MA: Addison-Wesley.Google Scholar
  47. Oliver, D. W., Kelliher, T. P., & Keegan, J. G. (1997). Engineering complex systems with models and objects. New York: McGraw-Hill.Google Scholar
  48. Pine, J. B. (1993). Mass customization: The new frontier in business competition. Cambridge, MA: Harvard Business School Press.Google Scholar
  49. Ribeiro, L., & Barata, J. (2013). Self-organizing multiagent mechatronic systems in perspective. In IEEE international conference on industrial informatics, IEEE, Bochum, Germany, 2013 11th IEEE International Conference on Industrial Informatics (INDIN) (pp. 392–397). doi: 10.1109/INDIN.2013.6622916.
  50. Setchi, R. M., & Lagos, N. (2005). Reconfigurability and reconfigurable manufacturing systems: State of the art review. In: Innovative production machines and systems: Production automation and control state of the art review (pp. 131–143). Cardiff, UK: Nework of Excellence, Innovative Production Machines and Systems.Google Scholar
  51. Shen, W., & Norrie, D. (1999). Agent-based systems for intelligent manufacturing: A state-of-the-art survey. Knowledge and Information Systems: An International Journal, 1(2), 129–156.CrossRefGoogle Scholar
  52. Shen, W., Norrie, D., & Barthes, J. P. (2000). MultiAgent systems for concurrent intelligent design and manufacturing. London: Taylor and Francis.CrossRefGoogle Scholar
  53. Shirinzadeh, B. (2002). Flexible fixturing for workpiece positioning and constraining. Assembly Automation, 22(2), 112–120. doi: 10.1108/01445150210423143.CrossRefGoogle Scholar
  54. Smith, S., Jiao, R., & Chu, C. H. (2013a). Editorial: Advances in mass customization. Journal of Intelligent Manufacturing, 24(5), 873–876. doi: 10.1007/s10845-012-0700-3.CrossRefGoogle Scholar
  55. Smith, S., Smith, G., Jiao, R., & Chu, C. H. (2013b). Mass customization in the product life cycle. Journal of Intelligent Manufacturing, 24(5), 877–885. doi: 10.1007/s10845-012-0691-0.CrossRefGoogle Scholar
  56. Stevens, S. S. (1946). On the theory of scales and measurement. Science, 103, 677–680.CrossRefGoogle Scholar
  57. Suh, N. P. (2001). Axiomatic design: Advances and applications. Oxford: Oxford University Press.Google Scholar
  58. Trappey, A., Trappey, C., & Ni, W. C. (2013). A multi-agent collaborative maintenance platform applying game theory negotiation strategies. Journal of Intelligent Manufacturing, 24(3), 613–623. doi: 10.1007/s10845-011-0606-5.
  59. Vallee, M., Merdan, M., Lepuschitz, W., & Koppensteiner, G. (2011). Decentralized reconfiguration of a flexible transportation system. IEEE Transactions on Industrial Informatics, 7(3), 505–516. doi: 10.1109/TII.2011.2158839.
  60. Viswanath, A., Baca, E. E. S., & Farid, A. M. (2013). An axiomatic design approach to passenger itinerary enumeration in reconfigurable transportation systems. IEEE Transactions on Intelligent Transportation Systems (99):1–10. doi: 10.1109/TITS.2013.2293340.
  61. Vyatkin, V. (2007). IEC 61499 function blocks for embedded and distributed control systems. Research Triangle Park, NC: Instrumentation Society of America.Google Scholar
  62. Wikipedia. (2007) Ontology. Technical report.Google Scholar
  63. Wu, D., Zhang, L., Jiao, R., & Lu, R. (2013). SysML-based design chain information modeling for variety management in production reconfiguration. Journal of Intelligent Manufacturing, 24(3), 575–596. doi: 10.1007/s10845-011-0585-6.
  64. Zuse, H. (1991). Software complexity: Measures and methods. Hawthorne, NJ: Walter de Gruyter & CoGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  1. 1.Engineering Systems & Management DepartmentMasdar Institute of Science & TechnologyAbu DhabiUAE
  2. 2.MIT Mechanical EngineeringCambridgeUSA

Personalised recommendations