Novel Computational Intelligence for Optimizing Cyber Physical Pre-evaluation System

Chapter
Part of the Studies in Computational Intelligence book series (SCI, volume 540)

Abstract

Owing to the quality heterogeneity of returned used products, firms engaged in re-manufacturing activities are obliged to employ 100 % inspection of these products to evaluate their quality and suitability for re-manufacturing. In addition to visual inspection, a recent tendency is to use data recorded in electronic devices (e.g., radio frequency identification (RFID)) implanted in the products. In this way, information is obtained quickly without the need for complete (and expensive) product disassembly. Nevertheless, making sense of RFID data in a complex cyber physical system (CPS) environment (which involves such as cloud computing for used product life cycle information retrieval and physically used products scanning) is a complex task. For instance, if an RFID readers fails, there may be missing values exist. The purpose of this chapter is to employ two computational intelligence (CI) optimization methods which can improve the reliability of such inspection process.

Keywords

Re-manufacturability Cyber physical pre-evaluation system Reliability-redundancy allocation problem Firefly algorithm Teaching–learning-based optimization Radio frequency identification 

References

  1. 1.
    M. AliIlgin, S.M. Gupta, Performance improvement potential of sensor embedded products in environmental supply chains. Resour. Conserv. Recycl. 55(6), 580–592 (2011).Google Scholar
  2. 2.
    T. Amezquita, R. Hammond, M. Salazar, B. Bras, Characterizing the re-manufacturability of engineering systems. Paper presented at the ASME advances in design automation conference, Boston, Massachusetts, USA, pp. 271–278, 17–20 Sept 1995Google Scholar
  3. 3.
    T. Apostolopoulos, A. Vlachos, Application of the firefly algorithm for solving the economic emissions load dispatch problem. Int. J.Combinatorics, Article ID 523806, 1–23 (2011)CrossRefGoogle Scholar
  4. 4.
    N. Aras, T. Boyaci, V. Verter, The effect of categorizing returned products in re-manufacturing. IIE Trans. 36, 319–331 (2004)CrossRefGoogle Scholar
  5. 5.
    B.G. Babu, M. Kannan, Lightning bugs. Resonance 7(9), 49–55 (2002)CrossRefGoogle Scholar
  6. 6.
    H.-G. Beyer, H.-P. Schwefel, Evolution strategies: a comprehensive introduction. J. Nat Comput. 1(1), 3–52 (2002)CrossRefMATHMathSciNetGoogle Scholar
  7. 7.
    J.D. Blackburn, V.D.R. Guide, G.C. Souza, L.N.V. Wassenhove, Reverse supply chains for commercial returns. Calif. Manag. Rev. 46(2), 6–22 (2004)CrossRefGoogle Scholar
  8. 8.
    B. Bras, M.W. McIntosh, Product, process, and organizational design for re-manufacture—an overview of research. Rob. Comput. Integr. Manuf. 15, 167–178 (1999)CrossRefGoogle Scholar
  9. 9.
    L.C. Cagnina, S.C. Esquivel, C.A. Coello, Solving engineering optimization problems with the simple constrained particle swarm optimizer. Informatica 32, 319–326 (2008)MATHGoogle Scholar
  10. 10.
    D.J. Cook, J.C. Augusto, V.R. Jakkula, Ambient intelligence: technologies, applications, and opportunities. Pervasive Mob. Comput. 5, 277–298 (2009)CrossRefGoogle Scholar
  11. 11.
    M. Črepinšek, S.-H. Liu, L. Mernik, A note on teaching–learning-based optimization algorithm. Inf. Sci. 212, 79–93 (2012)CrossRefGoogle Scholar
  12. 12.
    Y. Du, H. Cao, F. Liu, C. Li, X. Chen, An integrated method for evaluating the re-manufacturability of used machine tool. J. Clean. Prod. 20, 82–91 (2012)CrossRefGoogle Scholar
  13. 13.
    M. Fleischmann, H.R. Krikke, R. Dekker, S.D.P. Flapper, A characterisation of logistics networks for product recovery. OMEGA 28, 653–666 (2000)CrossRefGoogle Scholar
  14. 14.
    M. Fleischmann, JAEEv Nunen, B. Gräve, Integrating closed-loop supply chains and spare-parts management at IBM. Interfaces 33(6), 44–56 (2003)CrossRefGoogle Scholar
  15. 15.
    A.H. Gandomi, X.-S. Yang, A.H. Alavi, Mixed variable structural optimization using firefly algorithm. Comput. Struct. 89, 2325–2336 (2011)CrossRefGoogle Scholar
  16. 16.
    V.D.R. Guide, R.H. Teunter, L.N.V. Wassenhove, Matching demand supply to maximize profits from re-manufacturing. Manuf. Serv. Oper. Manage. 5(4), 303–316 (2003)Google Scholar
  17. 17.
    R. Hammond, T. Amezquita, B. Bras, Issues in the automotive parts re-manufacturing industry-a discussion of results from surveys performed among re-manufacturers. Int. J. Eng. Des. Autom.—Spec Issue Environmentally Conscious Des. Manufact. 4(1), 27–46 (1998)Google Scholar
  18. 18.
    M.-H. Horng, Vector quantization using the firefly algorithm for image compression. Expert. Syst. Appl. 39, 1078–1091 (2012)CrossRefGoogle Scholar
  19. 19.
    M.-H. Horng, Y.-X. Lee, M.-C. Lee, R.-J. Liou, Firefly meta-heuristic algorithm for training the radia basis function network for data classification and disease diagnosis. in ed. by R. Parpinelli Theory and New Applications of Swarm Intelligence, Chapter 7, pp. 115–132: In-Tech (2012)Google Scholar
  20. 20.
    M.-H. Horng, R.-J. Liou, Multilevel minimum cross entropy threshold selection based on the firefly algorithm. Expert. Syst. Appl. 38, 14805–14811 (2011)CrossRefGoogle Scholar
  21. 21.
    Y.-C. Hsieh, P.-S. You, An effective immune based two-phase approach for the optimal reliability. Appl. Math. Comput. 218, 1297–1307 (2011)CrossRefMathSciNetGoogle Scholar
  22. 22.
    J. Jumadinova, P. Dasgupta, Firefly-inspired synchronization for improved dynamic pricing in online markets. Paper presented at the 2nd IEEE international conference on self-adaptive and self-organizing Systems, pp. 402–412, (2008)Google Scholar
  23. 23.
    Ö. Karaer, H.L. Lee, Managing the reverse channel with RFID-enabled negative demand information. Prod. Oper. Manage. 16(5), 625–645 (2007)CrossRefGoogle Scholar
  24. 24.
    M. Kärkkäinen, T. Ala-Risku, K. Främling, The product centric approach: a solution to supply network information management problems? Comput. Ind. 52(2), 147–159 (2003)CrossRefGoogle Scholar
  25. 25.
    M. Kärkkäinen, T. Ala-Risku, J. Holmström, Increasing customer value and decreasing distribution costs with merge-in-transit. Int. J. Phys. Distrib. Logistics. Manage. 33(2), 132–148 (2003)CrossRefGoogle Scholar
  26. 26.
    J. Kennedy, R. C. Eberhart, Particle swarm optimization. Paper presented at the IEEE International Joint conference on neural networks (1995)Google Scholar
  27. 27.
    H. Krikke, I. l. Blanc, S. v. d. Velde, Product modularity and the design of closed-loop supply chains. Calif. Manage. Rev. 46(2), 23–38 (2004)Google Scholar
  28. 28.
    A. Kulkarni, D. Ralph, D. McFarlane, Value of RFID in re-manufacturing. Int. J. Serv. Oper. Inf. 2(3), 225–252 (2007)Google Scholar
  29. 29.
    A. G. Kulkarni, A. K. N. Parlikad, D. C. McFarlane, M. Harrison, Networked RFID systems in product recovery management. Paper presented at the IEEE international symposium on electronics and the environment (ISEE 2005), pp. 66–71 (2005)Google Scholar
  30. 30.
    W. Kuo, V.R. Prasad, An annotated overview of system-reliability optimization. IEEE Trans. Reliab. 49, 176–187 (2000)CrossRefGoogle Scholar
  31. 31.
    R. Leidenfrost, W. Elmenreich, Establishing wireless time-triggered communication using firefly clock synchronization approach. Paper presented at the 2008 international workshop on intelligent solutions in embedded systems, pp. 1–8, (2008)Google Scholar
  32. 32.
    S. Łukasik, S. Żak, Firefly algorithm for continuous constrained optimization tasks Computational Collective Intelligence. Semantic Web, Social Networks and Multiagent Systems, LNCS 5796. (Berlin, Spinger-Verlag, 2009), pp. 97–106Google Scholar
  33. 33.
    Z. Michalewicz, Genetic Algorithms + Data Structures = Evolution Programs, 3rd edn. (Springer-Verlag, Berlin Heidelberg, 1996)MATHGoogle Scholar
  34. 34.
    J. A. E. E. v. Nunen, R. Zuidwijk, E-enabled closed-loop supply chains. Calif. Manage. Rev. 46(2), 40–54, (2004)Google Scholar
  35. 35.
    R.V. Rao, V.J. Savsani, D.P. Vakharia, Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Comput. Aided. Des. 43, 303–315 (2011)CrossRefGoogle Scholar
  36. 36.
    M.K. Sayadi, R. Ramezanian, N. Ghaffari-Nasab, A discrete firefly meta-heuristic with local search for makespan minimization in permutation flow shop scheduling problems. Int. J. Ind. Eng. Comput. 1, 1–10 (2010)CrossRefGoogle Scholar
  37. 37.
    J. Senthilnath, S.N. Omkar, V. Mani, Clustering using firefly algorithm: performance study. Swarm Evol. Comput. 1, 164–171 (2011)CrossRefGoogle Scholar
  38. 38.
    L. Shu, W. Flowers, A structured approach to design for re-manufacture. Intell. Concurrent Des.: Fundam. Methodol. Model. Pract. 66, 13–19 (1993)Google Scholar
  39. 39.
    R. Storn, K. Price, Differential evolution—a simple and efficient heuristic for global optimization over continuous spaces. J. Global. Optim. 11(4), 341–359 (1997)CrossRefMATHMathSciNetGoogle Scholar
  40. 40.
    E. Sundin, B. Bras, Making functional sales environmentally and economically beneficial through product re-manufacturing. J. Clean. Prod. 13, 913–925 (2005)CrossRefGoogle Scholar
  41. 41.
    L. Wang, L.-P. Li, A coevolutionary differential evolution with harmony search for reliability–redundancy optimization. Expert. Syst. Appl. 39, 5271–5278 (2012)CrossRefGoogle Scholar
  42. 42.
    X. Wu, Research on design management based on green re-manufacturing engineering. Syst. Eng. Procedia 4, 448–454 (2012)CrossRefGoogle Scholar
  43. 43.
    B. Xing, W.-J. Gao, Computational intelligence in re-manufacturing. 701 E. Chocolate Avenue, Suite 200, Hershey PA 17033: IGI Global, ISBN 978-1-4666-4908-8 (2014)Google Scholar
  44. 44.
    B. Xing, W.-J. Gao, T. Marwala, The applications of computational intelligence in radio frequency identification research. Paper presented at the IEEE international conference on systems, man, and cybernetics (IEEE SMC), 14–17 Oct, (Seoul, Korea, 2012) pp. 2067–2072Google Scholar
  45. 45.
    X.-S. Yang, Nature-inspired metaheuristic algorithms 2nd edn. (UK, Luniver Press) ISBN 978-1-905986-28-6 (2008)Google Scholar
  46. 46.
    X.-S. Yang, Firefly algorithms for multimodal optimization, in SAGA 2009, LNCS 5792, ed. by O. Watanabe, T. Zeugmann (Springer-Verlag, Berlin Heidelberg, 2009), pp. 169–178Google Scholar
  47. 47.
    X.-S. Yang, Firefly algorithm, stochastic test functions and design optimisation. Int. J. Bio-Inspired Comput. 2(2), 78–84 (2010)CrossRefGoogle Scholar
  48. 48.
    X.-S. Yang, Chaos-enhanced firefly algorithm with automatic parameter tuning. Int. J. Swarm Intell. Res. 2(4), 1–11 (2011)CrossRefGoogle Scholar
  49. 49.
    J. Yao, S. Zhu, The research of design system for re-manufacturing. N. Technol. N. Process 5, 22–24 (2004)Google Scholar
  50. 50.
    W.-C. Yeh, T.-J. Hsieh, Solving reliability redundancy allocation problems using an artificial bee colony algorithm. Comput. Oper. Res. 38, 1465–1473 (2011)CrossRefMathSciNetGoogle Scholar
  51. 51.
    H. Yüksel, Design of automobile engines for re-manufacture with quality function deployment. Int. J. Sustain. Eng. 3(3), 170–180 (2010)CrossRefGoogle Scholar
  52. 52.
    Z.-N. Zhang, Z.-L. Liu, Y. Chen, Y.-B. Xie, Knowledge flow in engineering design: an ontological framework. Proc. Ins. Mech. Eng. Part C: J. Mech. Eng. Sci. 227(4), 760–770 (2013)CrossRefGoogle Scholar
  53. 53.
    C. Zikopoulos, G. Tagaras, On the attractiveness of sorting before disassembly in re-manufacturing. IIE Trans. 40, 313–323 (2008)CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media Singapore 2014

Authors and Affiliations

  1. 1.Department of Mechanical and Aeronautical Engineering, Faculty of Engineering, Built Environment and Information TechnologyUniversity of PretoriaPretoriaSouth Africa

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