Table of Contents
ISRN Aerospace Engineering
Volume 2014 (2014), Article ID 204546, 13 pages
http://dx.doi.org/10.1155/2014/204546
Research Article

Condition Based Maintenance Optimization of an Aircraft Assembly Process Considering Multiple Objectives

1Shanghai Aircraft Manufacturing Co., Ltd., Shanghai 200436, China
2Integrated Vehicle Health Management Centre, Cranfield University, Bedford MK43 0AL, UK
3Division of Engineering Sciences, Cranfield University, Bedford MK43 0AL, UK

Received 31 October 2013; Accepted 29 December 2013; Published 11 February 2014

Academic Editors: V. G. M. Annamdas, C. Bigelow, R. V. Rao, Y. Shi, and A. Yesildirek

Copyright © 2014 J. Li et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. MTorres, Total Solution for Aircraft Automatic Assembly Jigs, MTorres, Santa Ana, Calif, USA, 2013.
  2. P. Lute, An Investigation of Airbus A380 Stage 01 Wing Box Assembly Using Discrete Event Simulation, Cranfield University, Bedfordshire, UK, 2007.
  3. Electroimpact Inc., A380 Stage 1 GRAWDE Machine, Electroimpact Inc., Mukilteo, Wash, USA, 2003.
  4. IBM Corporation, Predictive Maintenance for Manufacturing, 2011.
  5. Wikipedia, “Preventive maintenance,” Wikipedia, http://en.wikipedia.org/wiki/Preventive_maintenance.
  6. ResolveFM, “Preventive/corrective maintenance,” ResolveFM, http://www.resolve.com.au/.
  7. A. Kelly, Maintenance Strategy, Butterworth-Heinemann, Oxford, UK, 1997.
  8. NACE, “Maintenance strategies,” 2013, http://www.nace.org/, http://events.nace.org/library/corrosion/Inspection/Strategies.asp.
  9. I. Grigoryev, AnyLogic 6 in Three Days: A Quick Course in Simulation Modeling, Anylogic North America, 2012.
  10. J. B. Leger, E. Neunreuthe, B. Iung, and G. Morel, “Integration of the predictive maintenance in manufacturing system,” in Advanced in Manufacturing, pp. 133–144, Springer, London, UK, 1999. View at Publisher · View at Google Scholar
  11. Z. Tian, D. Lin, and B. Wu, “Condition based maintenance optimization considering multiple objectives,” Journal of Intelligent Manufacturing, vol. 23, no. 2, pp. 333–340, 2009. View at Publisher · View at Google Scholar · View at Scopus
  12. J. Yulan, J. Zuhua, and H. Wenrui, “Multi-objective integrated optimization research on preventive maintenance planning and production scheduling for a single machine,” International Journal of Advanced Manufacturing Technology, vol. 39, no. 9-10, pp. 954–964, 2008. View at Publisher · View at Google Scholar · View at Scopus
  13. D. Achermann, Modelling, Simulation and Optimization of Maintenance Strategies Under Consideration of Logistic Processes, Südwestdeutscher, 2008.
  14. Š. Valčuha, A. Goti, J. Úradníček, and I. Navarro, “Multi-equipment condition based maintenance optimization by multi-objective genetic algorithm,” Journal of Achievements in Materials and Manufacturing Engineering, vol. 45, no. 2, pp. 188–193, 2011. View at Google Scholar
  15. L. Tautou and H. Pierreval, “Using evolutionary algorithms and simulation for the optimization of manufacturing systems,” IIE Transactions, vol. 29, no. 3, pp. 181–189, 1997. View at Google Scholar · View at Scopus
  16. D. Baglee, “Maintenance strategy development in the UK food and drink industry,” International Journal of Strategic Engineering Asset Management, vol. 1, no. 3, pp. 289–300, 2013. View at Google Scholar
  17. J. Reimann, G. Kacprzynski, D. Cabral, and R. Marini, “Using condition based maintenance to improve the profitability of performance based logistic contracts,” in Proceedings of the Annual Conference of the Prognostics and Health Management Society, 2009.
  18. A. Grall, C. Bérenguer, and L. Dieulle, “A condition-based maintenance policy for stochastically deteriorating systems,” Reliability Engineering and System Safety, vol. 76, no. 2, pp. 167–180, 2002. View at Publisher · View at Google Scholar · View at Scopus
  19. M. Rolón and E. Martínez, “Agent-based modeling and simulation of an autonomic manufacturing execution system,” Computers in Industry, vol. 63, no. 1, pp. 53–78, 2012. View at Publisher · View at Google Scholar
  20. W. Shen, Q. Hao, H. J. Yoon, and D. H. Norrie, “Applications of agent-based systems in intelligent manufacturing: an updated review,” Advanced Engineering Informatics, vol. 20, no. 4, pp. 415–431, 2006. View at Publisher · View at Google Scholar · View at Scopus
  21. M. A. Majid, U. Aickelin, and P. O. Siebers, “Comparing simulation output accuracy of discrete event and agent based models: a quantitave approach,” in Proceedings of the Summer Computer Simulation Conference (SCSC '09), Vista, Calif, USA, 2009.
  22. P. O. Siebers, C. M. MacAl, J. Garnett, D. Buxton, and M. Pidd, “Discrete-event simulation is dead, long live agent-based simulation!,” Journal of Simulation, vol. 4, no. 3, pp. 204–210, 2010. View at Publisher · View at Google Scholar · View at Scopus
  23. L. Holst, Integrating Discrete-Event Simulation into the Manufacturing System Development Process, Division of Robotics, Lund, Sweden, 2001.
  24. J. A. B. Montevechi, R. d. C. Miranda, and J. D. Friend, “Sensitivity analysis in discrete-event simulation using design of experiments,” in Discrete Event Simulations-Development and Applications, InTech, Rijeka, Croatia, 2012. View at Google Scholar
  25. Y. Carson and A. Maria, “Simulation optimization: methods and applications,” in Proceedings of the Winter Simulation Conference, pp. 118–126, Atlanta, Ga, USA, December 1997. View at Scopus
  26. R. Martí and M. Laguna, “Scatter search: basic design and advanced strategies,” Revista Iberoamericana de Inteligencia Artificial, vol. 7, no. 19, pp. 123–130, 2003. View at Google Scholar
  27. A. Ghosh and S. Dehuri, “Evolution algorithms for multi-criterion optimization: a survey,” International Journey of Computing and Information Sciences, vol. 2, no. 1, 2004. View at Google Scholar
  28. M. Laguna, R. Martí, M. Gallego, and A. Duarte, “The scatter search methodology,” in Wiley Encyclopedia of Operations Research and Management Science, Wiley-Blackwell, Hoboken, NJ, USA, 2011. View at Publisher · View at Google Scholar
  29. COMAC, “ARJ21 regional jet program,” 2013, http://english.comac.cc/products/rj/pi2/index.shtml.
  30. Electroimpact, Flex Track, Electroimpact, Mukilteo, Wash, USA, 2013.
  31. IEEE/PES Task Force on Impact of Maintenance Strategy on Reliability of the Reliability, Risk and Probability Applications Subcommittee, S. Aboresheid, R. N. Allan et al., “The present status of maintenance strategies and the impact of maintenance on reliability,” IEEE Transactions on Power Systems, vol. 16, no. 4, pp. 638–646, 2001. View at Publisher · View at Google Scholar · View at Scopus
  32. M. Kaegi, R. Mock, and W. Kröger, “Analyzing maintenance strategies by agent-based simulations: a feasibility study,” Reliability Engineering and System Safety, vol. 94, no. 9, pp. 1416–1421, 2009. View at Publisher · View at Google Scholar · View at Scopus
  33. A. Borshchev, Designing State-Based Behavior: Statecharts, Anylogic, 2013.
  34. Process Engineering Group, Introduction to SSm, Instituto de Investigaciones Marinas (C.S.I.C.), Vigo, Spain, 2009.
  35. J. April, F. Glover, J. P. Kelly, and M. Laguna, “Practical introduction to simulation optimization,” in Proceedings of the Winter Simulation Conference, pp. 71–78, Boulder, Colo, USA, December 2003. View at Scopus
  36. W. Abo-Hamad and A. Arisha, “Simulation optimisation methods in supply chain applications: a review,” Irish Journal of Management, vol. 30, no. 2, pp. 95–124, 2011. View at Google Scholar
  37. C.-H. Chen and L. H. Lee, “Introduction to stochastic simulation optimization,” in Stochastic Simulation Optimization: An Optimal Computing Budget Allocation, System Engineering and Operations Research, World Scientific, Hackensack, NJ, USA, 2010. View at Google Scholar
  38. M. Laguna, OptQuest: Optimization of Complex Systems, OptTek Systems, 2011.
  39. R. Martí, M. Laguna, and F. Glover, “Principles of scatter search,” European Journal of Operational Research, vol. 169, no. 2, pp. 359–372, 2006. View at Publisher · View at Google Scholar · View at Scopus
  40. OptTek, “How the OptQuest engine works,” OptTek, http://www.opttek.com.
  41. F. Glover and A. Reinholz, “Metaheuristics in science and industry: new developments,” in Proceedings of the Metaheuristics International Conference, Montreal, Canada, June 2007.
  42. I. Boussaïd, J. Lepagnot, and P. Siarry, “A survey on optimization metaheuristics,” Information Sciences, vol. 237, pp. 82–117, 2013. View at Publisher · View at Google Scholar
  43. K. Deb, Multi-Objective Optimization Using Evolutionary Algorithms, Wiley-Blackwell, Chichester, UK, 2001.
  44. M. Gen and R. Cheng, Genetic Algorithms and Engineering Optimization, John Wiley & Sons, New York, NY, USA, 2000.
  45. A. Ghosh and S. Dehui, “Evolutionary algorithms for multi-criterion optimization: a survey,” International Journal of Computing and Information Sciences, vol. 2, no. 1, pp. 38–57, 2004. View at Google Scholar
  46. E. J. Hughes, “Multiple single objective pareto sampling,” Evolutionary Computation, vol. 4, pp. 2678–2684, 2003. View at Google Scholar
  47. T. Screenuch, A. Tsourdos, E. J. Hughes, and B. A. White, “Fuzzy gain-scheduled missile autopilot design using evolutionary algorithms,” IEEE Transactions on Aerospace and Electronic Systems, vol. 42, no. 4, pp. 1323–1339, 2006. View at Publisher · View at Google Scholar · View at Scopus
  48. Y. Jin and J. Branke, “Evolutionary optimization in uncertain environments—a survey,” IEEE Transactions on Evolutionary Computation, vol. 9, no. 3, pp. 303–317, 2005. View at Publisher · View at Google Scholar · View at Scopus
  49. J. E. Fieldsend and R. M. Everson, “Multi-objective optimisation in the presence of uncertainty,” in Proceedings of the IEEE Congress on Evolutionary Computation, vol. 1, pp. 243–250, Edinburgh, UK, September 2005. View at Scopus
  50. OptTek, “Multi-objective optimization,” OptTek, http://www.opttek.com.