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Journal of Applied Sciences

Year: 2013 | Volume: 13 | Issue: 22 | Page No.: 4922-4928
DOI: 10.3923/jas.2013.4922.4928
Assembly Sequence Planning of Quayside Container Crane Based on Improved Immune Algorithm
Houjun Lu, Huiqiang Zhen, Youfang Huang and Wei Yan

Abstract: Quayside container crane is one kind of large scale heavy product. Good assembly plan will minimize the cost of manufacturer and to ensure the safety of assembly operation in container terminal. In this study, we have extensively investigated a novel approach to automatically generate the assembly sequences for industrial field which is especially applied to other large-scale structures. The approach is proposed to find the optimum assembly sequence which will integrate the genetic operators into the immune algorithm. The approach will take advantages of the immune memory for local optimum search and improve the global search ability of immune algorithm by the genetic operators. An example of one type of container quay crane illustrates the use of the physically based approach to generate assembly sequence which shows the efficiency and the operability to guide the assembly work.

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How to cite this article
Houjun Lu, Huiqiang Zhen, Youfang Huang and Wei Yan, 2013. Assembly Sequence Planning of Quayside Container Crane Based on Improved Immune Algorithm. Journal of Applied Sciences, 13: 4922-4928.

Keywords: Immune algorithm, genetic operator, quay crane and assembly sequence planning

REFERENCES
  • Sinanoglu, C. and H.R. Borklu, 2005. An assembly sequence-planning system for mechanical parts using neural network. Assembly Automation, 25: 38-52.
    CrossRef    Direct Link    

  • Cao, P.B. and R.B. Xiao, 2007. Assembly planning using a novel immune approach. Int. J. Adv. Manufacturing Technol., 31: 770-782.
    CrossRef    Direct Link    

  • Cui, X.X., 2008. Review of the Research on Multi-Objective Evolutionary Algorithm. National Defense Industry Press, USA

  • Kavraki, L., J.C. Latombe and R.H. Wilson, 1993. On the complexity of assembly partitioning. Inform. Process. Lett., 48: 229-235.
    CrossRef    Direct Link    

  • Kaya, M., 2011. The effects of a new selection operator on the performance of a genetic algorithm. Applied Math. Comput., 217: 7669-7678.
    CrossRef    Direct Link    

  • Marian, R.M., L.H.S. Luong and K. Abhary, 2006. A genetic algorithm for the optimization of assembly sequences. Comput. Ind. Eng., 50: 503-527.
    CrossRef    Direct Link    

  • Shan, H., S. Zhou and Z. Sun, 2009. Research on assembly sequence planning based on genetic simulated annealing algorithm and ant colony optimization algorithm. Assembly Automation, 29: 249-256.
    CrossRef    Direct Link    

  • Wolter, J.D., 1989. On the automatic generation of assembly plans. Proceedings of the International Conference on Robotics and Automation, Volume 1, May 14-19, 1989, Scottsdale, AZ., pp: 62-68.

  • Wolter, J.D., 1991. A combinatorial analysis of enumerative data structures for assembly planning. Proceedings of the International Conference on Robotics and Automation, Volume 1, April 9-11, 1991, Sacramento, AZ., pp: 611-618.

  • Wilson, R.J. and J.J. Watkins, 1990. Graphs: An Introductory Approach: A First Course in Discrete Mathematics. Wiley, John Sons, New York, ISBN: 9780471513407, Pages: 340

  • Wilson, R.H., L. Kavraki, J.C. Latombe and T. Lozano-Perez, 1995. Two-handed assembly sequencing. Int. J. Robotics Res., 14: 335-350.
    CrossRef    Direct Link    

  • Tseng, Y.J., H.T. Kao and F.Y. Huang, 2010. Integrated assembly and disassembly sequence planning using a GA approach. Int. J. Prod. Res., 48: 5991-6013.
    CrossRef    Direct Link    

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