Cluster Computing

, Volume 22, Supplement 1, pp 835–846 | Cite as

Assembly sequence planning method based on particle swarm algorithm

  • Yu-jia WuEmail author
  • Yan Cao
  • Qiang-feng Wang


In the paper, PSO algorithm is used to solve the assembly sequence planning problem. According to the analysis and extraction of fixture assembly information, a complete and correct fixture assembly model is established in which PSO algorithm is introduced, including assembly direction matrix, interference matrix, sequence-relation matrix, etc. Taking shorten the assembly time as the optimization goal, the feasible assembly sequences for specific fixture are obtained using PSO algorithm and the optimal assembly sequence is found. The influence of main factors on PSO algorithm is analyzed. With the increase of population, the chance to find the optimal solution increases. When \(\upomega \) and \(\hbox {c}_{1}\) increase and \(\hbox {c}_{2}\) decreases, it is good for global searches of the PSO algorithm. When \(\upomega \) and \(\hbox {c}_{1}\) decrease, and \(\hbox {c}_{2}\) increases, it is good for local searches of the PSO algorithm. In practical applications, the factors should be adjusted according to specific problems.


Assembly sequence Particle swarm algorithm Fixture Factor analysis 


  1. 1.
    Wan, J.: Research on Knowledge and Coding of Assembly Sequence Planning [D]. Huazhong University of Science and Technology, Wuhan (2015)Google Scholar
  2. 2.
    Kashkoush, M., ElMaraghy, H.: Knowledge-based model for constructing master assembly sequence [J]. J. Manuf. Syst. 34, 43–52 (2015)CrossRefGoogle Scholar
  3. 3.
    Yu, H., Shui, L.: Assembly sequence planning based on improved particle swarm optimization algorithm [J]. J. Shenyang Ligong Univ. 34(4), 29–33 (2015)Google Scholar
  4. 4.
    Mi, K., Hu, Y., Yin, C.: Quality evaluation for model based definition of aerospace products [J]. Adv. Mater. Res. 945, 30–34 (2014)CrossRefGoogle Scholar
  5. 5.
    Zhang, H., Liu, H., Li, L.: Research on a kind of assembly sequence planning based on immune algorithm and particle swarm optimization algorithm [J]. Int. J. Adv. Manuf. Technol. 71, 5–8 (2014)Google Scholar
  6. 6.
    Wang, H., Rong, Y., Xiang, D.: Mechanical assembly planning using ant colony optimization [J]. Comput. Aided Des. 47, 59–71 (2014)CrossRefGoogle Scholar
  7. 7.
    Gao, G.: A Constraint Approximation assisted PSO for Computationally Expensive Constrained Problems [D]. Taiyuan University of Science & Technology, Taiyuan (2014)CrossRefGoogle Scholar
  8. 8.
    Zhang, H., Zhou, L., Zhang, P., Zhang, S.: Research on assembly sequence planning for complex assembly model [J]. Ship Eng. 38(7), 84–88 (2016)Google Scholar
  9. 9.
    Wang, J., Li, M., Li, S.: Assembly sequence planning based on combined nested partitions algorithm [J]. Mach. Manuf. 01, 39–42 (2017)Google Scholar
  10. 10.
    Liu, D., Zhang, W., Lu, B.: Assembly sequence planning based on various population strategy-particle swarm optimization algorithm [J]. Modul. Mach. Tool Autom. Manuf. Tech. 2, 30–33 (2017)Google Scholar
  11. 11.
    Zeng, B., Li, M., Zhang, Y.: Assembly sequence planning based on improved firefly algorithm [J]. Comput. Integr. Manuf. Syst. 04, 799–806 (2014)Google Scholar
  12. 12.
    Li, M.: Research on Methods of Assembly Sequence Planning for Complex Product [D]. Huazhong University of Science and Technology, Hubei (2013)Google Scholar
  13. 13.
    Yu, M., Gu, T., Chang, L., Li, F.: Assembly ontology for assembly sequence planning [J]. Pattern Recognit. Artif. Intell. 03, 204–215 (2016)Google Scholar
  14. 14.
    Tuppadung, Y., Kurutach, W.: Comparing nonlinear inertia weights and constriction factors in particle swarm optimization [J]. Int. J. Knowl. Based Intell. Eng. Syst. 15(2), 65–70 (2011)CrossRefGoogle Scholar
  15. 15.
    Sun, Z., Liu, T., Wang, J., et al.: An improved particle swarm optimization based on adaptive mutation and P systems for micro-grid economic operation [J]. Lect. Notes Electr. Eng. 336, 505–512 (2015)CrossRefGoogle Scholar
  16. 16.
    He, T., Wang, J., Zhang, S., et al.: Quantum particle swarm optimization based on P systems for applications in the economic operation of micro-grid [M]. In: Proceedings of the 2015 Chinese Intelligent Automation Conference. Springer, Berlin, pp. 521–529 (2015)Google Scholar
  17. 17.
    Senthil Arumugam, M., Rao, M.V.C., Tan, A.W.C.: A novel and effective particle swarm optimization like algorithm with extrapolation technique. Appl. Soft Comput. 9(1), 308–320 (2009)CrossRefGoogle Scholar
  18. 18.
    Wang, X., Zhang, G., Zhao, J., et al.: A modified membrane-inspired algorithm based on particle swarm optimization for mobile robot path planning [J]. Int. J. Comput. Commun. Control 10(5), 732–745 (2015)CrossRefGoogle Scholar
  19. 19.
    Lenin, K., Reddy, B.R.: Voltage enhancement and reduction of real power loss by particle swarm optimization algorithm based on membrane computing [J]. J. Ind. Intell. Inf. 3(3), 1 (2015)Google Scholar
  20. 20.
    Wang, J., Peng, H., Tu, M., et al.: A fault diagnosis method of power systems based on an improved adaptive fuzzy spiking neural P systems and PSO algorithms [J]. Chin. J. Electron. 25(2), 320–327 (2016)CrossRefGoogle Scholar
  21. 21.
    Zahara, E., Kao, Y.-T.: Hybrid Nelder-Mead simplex search and particle swarm optimization for constrained engineering design problems. Expert Syst. Appl. 36(2), 3880–3886 (2009)CrossRefGoogle Scholar
  22. 22.
    Vasile, C.I., Pavel, A.B., Dumitrache, I., et al.: On the power of enzymatic numerical P systems [J]. Acta Inf. 49(6), 395–412 (2012)MathSciNetCrossRefzbMATHGoogle Scholar
  23. 23.
    Mingyu, L., Bo, W., Youmin, H.: The application of hybrid algorithm to the assembly sequence planning. Mech. Sci. Technol. Aerosp. Eng. 05, 647–651 (2014)Google Scholar
  24. 24.
    Liu, H., Li, L., Zhang, H.: Assembly sequence planning for lithium-ion battery modules based on improved particle swarm optimization algorithm. Chin. J. Constr. Mach. 08, 306–312 (2014)Google Scholar
  25. 25.
    Xu, Z., Yin, W.: Assembly sequence planning based on orientation matrix. Mach. Build. Autom. 02, 55–58 (2015)Google Scholar
  26. 26.
    Liu, X., Chen, H., Zhang, S.: Research on concurrent assembly sequence planning based on subassembly. CAD/CAM/CAE/CAPP Manuf. Inf. 02, 99–102 (2015)Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2017

Authors and Affiliations

  1. 1.Xi’an Technological UniversityXi’anChina

Personalised recommendations