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Challenges and Properties for Bio-inspiration in Manufacturing

  • João Dias Ferreira
  • Luis Ribeiro
  • Mauro Onori
  • José Barata
Part of the IFIP Advances in Information and Communication Technology book series (IFIPAICT, volume 423)

Abstract

The increasing market fluctuations and customized products demand have dramatically changed the focus of industry towards organizational sustainability and supply chain agility. Such critical changes inevitably have a direct impact on the shop-floor operational requirements. In this sense, a number of innovative production paradigms emerged, providing the necessary theoretical background to such systems. Due to similarities between innovative modular production floors and natural complex systems, modern paradigms theoretically rely on bio-inspired concepts to attain the characteristics of biological systems. Nevertheless, during the implementation phase, bio-inspired principles tend to be left behind in favor of more traditional approaches, resulting in simple distributed systems with considerable limitations regarding scalability, reconfigurable ability and distributed problem resolution.

This paper analyzes and presents a brief critical review on how bio-inspired concepts are currently being explored in the manufacturing environment, in an attempt to formulate a number of challenges and properties that need to be considered in order to implement manufacturing systems that closely follow the biological principles and consequently present overall characteristics of complex natural systems.

Keywords

Bio-inspiration Self-Organization Manufacturing Systems 

References

  1. 1.
    Ueda, K.: A concept for bionic manufacturing systems based on dna-type information. In: Proc. of the IFIP TC5/WG5. 3 Eight International PROLAMAT Conference on Human Aspects in Computer Integrated Manufacturing, pp. 853–863. North-Holland (1992)Google Scholar
  2. 2.
    Gou, L., Luh, P.B., Kyoya, Y.: Holonic manufacturing scheduling: architecture, cooperation mechanism, and implementation. Computers in Industry 37(3), 213–231 (1998)CrossRefGoogle Scholar
  3. 3.
    Bussmann, S., McFarlane, D.C.: Rationales for holonic manufacturing control. In: Proc. of Second Int. Workshop on Intelligent Manufacturing Systems, pp. 177–184 (1999)Google Scholar
  4. 4.
    Koren, Y., Heisel, U., Jovane, F., Moriwaki, T., Pritschow, G., Ulsoy, G., Van Brussel, H.: Reconfigurable manufacturing systems. CIRP Annals-Manufacturing Technology 48(2), 527–540 (1999)CrossRefGoogle Scholar
  5. 5.
    Mehrabi, M.G., Ulsoy, A.G., Koren, Y.: Reconfigurable manufacturing systems and their enabling technologies. International Journal of Manufacturing Technology and Management 1(1), 114–131 (2000)CrossRefGoogle Scholar
  6. 6.
    Onori, M.: Evolvable assembly systems - a new paradigm? In: 33rd Int. Symposium on Robotics (ISR), pp. 617–621 (2002)Google Scholar
  7. 7.
    Onori, M., Alsterman, H., Barata, J.: An architecture development approach for evolvable assembly systems. In: 6th IEEE Int. Symposium on Assembly and Task Planning: From Nano to Macro Assembly and Manufacturing (ISATP 2005), pp. 19–24. IEEE (2005)Google Scholar
  8. 8.
    Ferreira, J.D.: Bio-inspired Self-Organisation in Evolvable Production Systems. Tekn. Lic. dissertation, Royal Institute of Technology, Sweden (2013)Google Scholar
  9. 9.
    Holland, J.H.: Emergence: From chaos to order. Oxford University Press (2000)Google Scholar
  10. 10.
    Ribeiro, L., Barata, J.: Self-organizing multiagent mechatronic systems in perspective. In: 2013 11th IEEE International Conference on Industrial Informatics, INDIN (2013)Google Scholar
  11. 11.
    Floreano, D., Mattiussi, C.: Bio-inspired artificial intelligence: theories, methods, and technologies. The MIT Press (2008)Google Scholar
  12. 12.
    Kumar, V., Murthy, A., Chandrashekara, K.: Scheduling of flexible manufacturing systems using genetic algorithm: A heuristic approach. J. Ind. Eng. Int. 7(14), 7–18 (2011)Google Scholar
  13. 13.
    Wang, L., Zheng, D.: A modified evolutionary programming for flow shop scheduling. The International J. Advanced Manufacturing Technology 22(7), 522–527 (2003)CrossRefGoogle Scholar
  14. 14.
    Zhang, F., Zhang, Y., Nee, A.: Using genetic algorithms in process planning for job shop machining. IEEE Tran. Evolutionary Computation 1(4), 278–289 (1997)CrossRefGoogle Scholar
  15. 15.
    Routroy, S., Kodali, R.: Differential evolution algorithm for supply chain inventory planning. Journal of Manufacturing Technology Management 16(1), 7–17 (2005)CrossRefGoogle Scholar
  16. 16.
    Brezocnik, M., Kovacic, M., Psenicnik, M.: Prediction of steel machinability by genetic programming. Journal of Achievements in Materials and Manufacturing Engineering 16(1-2), 107–113 (2006)Google Scholar
  17. 17.
    Stawowy, A.: Evolutionary strategy for manufacturing cell design. Omega 34(1), 1–18 (2006)CrossRefGoogle Scholar
  18. 18.
    Wu, T.-H., Chang, C.-C., Chung, S.-H.: A simulated annealing algorithm for manufacturing cell formation problems. Expert Systems with Applications 34(3), 1609–1617 (2008)CrossRefGoogle Scholar
  19. 19.
    Chan, K., Kwong, C., Tsim, Y.: A genetic programming based fuzzy regression approach to modelling manufacturing processes. International Journal of Production Research 48(7), 1967–1982 (2010)CrossRefzbMATHGoogle Scholar
  20. 20.
    Chan, K., Kwong, C., Fogarty, T.: Modeling manufacturing processes using a genetic programming-based fuzzy regression with detection of outliers. Information Sciences 180(4), 506–518 (2010)CrossRefMathSciNetGoogle Scholar
  21. 21.
    Leitão, P., Restivo, F.: Adacor: A holonic architecture for agile and adaptive manufacturing control. Computers in Industry 57(2), 121–130 (2006)CrossRefGoogle Scholar
  22. 22.
    Solimanpur, M., Vrat, P., Shankar, R.: Ant colony optimization algorithm to the inter-cell layout problem in cellular manufacturing. European Journal of Operational Research 157(3), 592–606 (2004)CrossRefzbMATHMathSciNetGoogle Scholar
  23. 23.
    Yu, B., Yang, Z.-Z., Yao, B.: An improved ant colony optimization for vehicle routing problem. European Journal of Operational Research 196(1), 171–176 (2009)CrossRefzbMATHGoogle Scholar
  24. 24.
    Pham, D., Koc, E., Lee, J., Phrueksanant, J.: Using the bees algorithm to schedule jobs for a machine. In: Proceedings of Eighth International Conference on Laser Metrology, CMM and Machine Tool Performance, pp. 430–439 (2007)Google Scholar
  25. 25.
    Pham, D., Otri, S., Darwish, A.H.: Application of the bees algorithm to pcb assembly optimisation. In: 3rd International Virtual Conference on Intelligent Production Machines and Systems, IPROMS, pp. 511–516 (2007)Google Scholar
  26. 26.
    Atasagun, Y., Kara, Y.: Assembly line balancing using bacterial foraging optimization algorithm (2012)Google Scholar
  27. 27.
    Sanaei, P., Akbari, R., Zeighami, V., Shams, S.: Using firefly algorithm to solve resource constrained project scheduling problem. In: Bansal, J.C., Singh, P.K., Deep, K., Pant, M., Nagar, A.K. (eds.) BIC-TA 2012. AISC, vol. 201, pp. 417–428. Springer, Heidelberg (2013)Google Scholar
  28. 28.
    Sayadi, M.K., Hafezalkotob, A., Naini, S.G.J.: Firefly-inspired algorithm for discrete optimization problems: An application to manufacturing cell formation. Journal of Manufacturing Systems (2012)Google Scholar
  29. 29.
    Aungkulanon, P., Chai-Ead, N., Luangpaiboon, P.: Simulated manufacturing process improvement via particle swarm optimisation and firefly algorithms. In: Proceedings of the International MultiConference of Engineers and Computer Scientists, vol. 2 (2011)Google Scholar
  30. 30.
    Rangwala, S., Dornfeld, D.: Sensor integration using neural networks for intelligent tool condition monitoring. J. Engineering for Industry 112(3), 219–228 (1990)CrossRefGoogle Scholar
  31. 31.
    Sunil, V., Pande, S.: Automatic recognition of machining features using artificial neural networks. Int. J. Advanced Manufacturing Technology 41(9), 932–947 (2009)CrossRefGoogle Scholar
  32. 32.
    Eski, I., Erkaya, S., Savas, S., Yildirim, S.: Fault detection on robot manipulators using artificial neural networks. Robotics and Computer-Integrated Manufacturing 27(1), 115–123 (2011)CrossRefGoogle Scholar
  33. 33.
    Ong, Z.X., Tay, J.C., Kwoh, C.K.: Applying the clonal selection principle to find flexible job-shop schedules. In: Jacob, C., Pilat, M.L., Bentley, P.J., Timmis, J.I. (eds.) ICARIS 2005. LNCS, vol. 3627, pp. 442–455. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  34. 34.
    Ulutaş, B.H., Işlier, A.A.: Parameter setting for clonal selection algorithm in facility layout problems. In: Gervasi, O., Gavrilova, M.L. (eds.) ICCSA 2007, Part I. LNCS, vol. 4705, pp. 886–899. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  35. 35.
    Hao, X., Cai-xin, S.: Artificial immune network classification algorithm for fault diagnosis of power transformer. IEEE Trans. Power Delivery 22(2), 930–935 (2007)CrossRefGoogle Scholar
  36. 36.
    Dasgupta, D., Forrest, S.: Tool breakage detection in milling operations using a negative-selection algorithm. Technical Report CS95-5, Department of Computer Science, University of New Mexico, Tech. Rep. (1995)Google Scholar
  37. 37.
    Leitão, P., Barbosa, J., Trentesaux, D.: Bio-inspired multi-agent systems for reconfigurable manufacturing systems. Engineering Applications of Artificial Intelligence 25(5), 934–944 (2012)CrossRefGoogle Scholar

Copyright information

© IFIP International Federation for Information Processing 2014

Authors and Affiliations

  • João Dias Ferreira
    • 1
  • Luis Ribeiro
    • 2
  • Mauro Onori
    • 1
  • José Barata
    • 2
  1. 1.EPS Group, Dep. of Production EngineeringKungliga Tekniska HögskolanStockholmSweden
  2. 2.CTS, UNINOVA, Dep. de Eng. Electrotcnica, F.C.T.Universidade Nova de LisboaMonte da CaparicaPortugal

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