Skip to main content
SpringerLink
Log in

Assembly sequence planning based on an improved harmony search algorithm

  • ORIGINAL ARTICLE
  • Published:
The International Journal of Advanced Manufacturing Technology Aims and scope Submit manuscript

Abstract

As a typical, discrete, and NP-hard problem, assembly sequence planning (ASP) has direct impact on assembly quality and costs. So far, lots of graph-based and meta-heuristic approaches cannot solve this problem effectively. This paper proposes an effective ASP algorithm based on the harmony search (HS) algorithm, which has an outstanding global search ability to obtain the global optimum more efficiently. To solve the ASP problem, an improved harmony search (IHS) algorithm is proposed mainly in four aspects: (1) an encoding of harmony is designed based on ASP problems; (2) an initial harmony memory (HM) is established using the opposition-based learning (OBL) strategy; (3) a particular way to improvise a new harmony is developed; and (4) a local search strategy is introduced to accelerate the convergence speed. Finally, the advantage of the proposed ASP algorithm over the competing algorithms in solving ASP problems is verified by two experiments. Moreover, the proposed ASP algorithm is applied to a practical problem, a propeller of azimuth thruster to verify its practicability.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. de Azevedo JG, Arantes Filho AC, da Costa LEVL (2015) Fins module conception of the microsatellite launch vehicle based on design for manufacture and assembly method. J Aerosp Technol Manag 7(1):93–100

    Article  Google Scholar 

  2. Kim S, Baek J, Moon S, Jeon S (2015) A new approach for product design by integrating assembly and disassembly sequence structure planning. In: Handa H, Ishibuchi H, Ong Y-S, Tan KC (eds) Proceedings of the 18th Asia pacific symposium on intelligent and evolutionary systems, vol 1. Springer International Publishing, Switzerland, pp 247–257. doi:10.1007/978-3-319-13359-1_20

    Google Scholar 

  3. Ivanov D, Dolgui A, Sokolov B, Werner F, Ivanova M (2015) A dynamic model and an algorithm for short-term supply chain scheduling in the smart factory industry 4.0. Int J Prod Res ahead-of-print:1–17. doi: 10.1080/00207543.2014.999958

  4. Ghandi S, Masehian E (2015) A breakout local search (BLS) method for solving the assembly sequence planning problem. Eng Appl Artif Intel 39:245–266

    Article  Google Scholar 

  5. Rashid MFF, Hutabarat W, Tiwari A (2012) A review on assembly sequence planning and assembly line balancing optimisation using soft computing approaches. Int J Adv Manuf Technol 59(1–4):335–349. doi:10.1007/s00170-011-3499-8

    Article  Google Scholar 

  6. Marian RM, Luong LHS, Abhary K (2006) A genetic algorithm for the optimisation of assembly sequences. Comput Ind Eng 50(4):503–527. doi:10.1016/j.cie.2005.07.007

    Article  Google Scholar 

  7. Valle C, Gasca R, Toro M, Camacho E (2003) A genetic algorithm for assembly sequence planning. In: Mira J, Álvarez J (eds) Artificial neural nets problem solving methods, vol 2687. Springer Berlin Heidelberg, Berlin, pp 337–344. doi:10.1007/3-540-44869-1_43

    Chapter  Google Scholar 

  8. Wang HS, Che ZH, Chiang CJ (2012) A hybrid genetic algorithm for multi-objective product plan selection problem with ASP and ALB. Expert System Appl 39(5):5440–5450. doi:10.1016/j.eswa.2011.11.041

    Article  Google Scholar 

  9. Xing Y, Wang Y (2012) Assembly sequence planning based on a hybrid particle swarm optimisation and genetic algorithm. Int J Prod Res 50(24):7303–7312. doi:10.1080/00207543.2011.648276

    Article  Google Scholar 

  10. Li XY, Gao L, Wen X (2013) Application of an efficient modified particle swarm optimization algorithm for process planning. Int J Adv Manuf Technol 67(5–8):1355–1369

    Article  Google Scholar 

  11. Lv H, Lu C (2010) An assembly sequence planning approach with a discrete particle swarm optimization algorithm. Int J Adv Manuf Technol 50(5–8):761–770

    Article  Google Scholar 

  12. Wang Y, Liu JH (2010) Chaotic particle swarm optimization for assembly sequence planning. Robot Comput Integr Manuf 26(2):212–222. doi:10.1016/j.rcim.2009.05.003

    Article  Google Scholar 

  13. Zhang H, Liu H, Li L (2013) Research on a kind of assembly sequence planning based on immune algorithm and particle swarm optimization algorithm. Int J Adv Manuf Technol 71(5–8):795–808. doi:10.1007/s00170-013-5513-9

    Google Scholar 

  14. Gao L, Qian W, Li XY, Wang J (2009) Application of memetic algorithm in assembly sequence planning. Int J Adv Manuf Technol 49(9–12):1175–1184. doi:10.1007/s00170-009-2449-1

    Google Scholar 

  15. Tseng H-E, Wang W-P, Shih H-Y (2007) Using memetic algorithms with guided local search to solve assembly sequence planning. Expert Syst Appl 33(2):451–467. doi:10.1016/j.eswa.2006.05.025

    Article  Google Scholar 

  16. Yu J, Wang C (2013) A max–min ant colony system for assembly sequence planning. Int J Adv Manuf Technol 67(9–12):2819–2835. doi:10.1007/s00170-012-4695-x

    Article  Google Scholar 

  17. Shuang B, Chen J, Li Z (2008) Microrobot based micro-assembly sequence planning with hybrid ant colony algorithm. Int J Adv Manuf Technol 38(11–12):1227–1235

    Article  Google Scholar 

  18. Gao L, Zhang CJ, Li XY, Wang LJ (2014) Discrete electromagnetism-like mechanism algorithm for assembly sequences planning. Int J Prod Res 52(12):3485–3503. doi:10.1080/00207543.2013.867087

    Article  Google Scholar 

  19. Geem ZW, Kim JH, Loganathan G (2001) A new heuristic optimization algorithm: harmony search. Simulation 76(2):60–68. doi:10.1177/003754970107600201

    Article  Google Scholar 

  20. Arul R, Ravi G, Velusami S (2013) An improved harmony search algorithm to solve economic load dispatch problems with generator constraints. Electr Eng 96(1):55–63. doi:10.1007/s00202-012-0276-0

    Article  Google Scholar 

  21. Gil-Lopez S, Del Ser J, Landa I, Garcia-Padrones L, Salcedo-Sanz S, Portilla-Figueras J (2010) On the application of a novel grouping harmony search algorithm to the switch location problem. In: Chatzimisios P, Verikoukis C, Santamaría I, Laddomada M, Hoffmann O (eds) Mobile lightweight wireless systems, vol 45. Springer Berlin Heidelberg, Berlin, pp 662–672. doi:10.1007/978-3-642-16644-0_57

    Chapter  Google Scholar 

  22. Degertekin SO (2009) Optimum design of steel frames via harmony search algorithm. In: Geem Z (ed) Harmony search algorithms for structural design optimization, vol 239. Springer Berlin Heidelberg, Berlin, pp 51–78. doi:10.1007/978-3-642-03450-3_2

    Chapter  Google Scholar 

  23. Mahdavi M, Chehreghani MH, Abolhassani H, Forsati R (2008) Novel meta-heuristic algorithms for clustering web documents. Appl Math Comput 201(1):441–451

    MathSciNet  MATH  Google Scholar 

  24. Forsati R, Haghighat A, Mahdavi M (2008) Harmony search based algorithms for bandwidth-delay-constrained least-cost multicast routing. Comput Commun 31(10):2505–2519

    Article  Google Scholar 

  25. Farshbaf Zinati R, Razfar MR (2011) Constrained optimum surface roughness prediction in turning of X20Cr13 by coupling novel modified harmony search-based neural network and modified harmony search algorithm. Int J Adv Manuf Technol 58(1–4):93–107. doi:10.1007/s00170-011-3393-4

    Google Scholar 

  26. Türk S, Radeke R (2011) Optimization of energy efficient network migration using harmony search. In: Lehnert R (ed) Energy-aware communications, vol 6955. Springer Berlin Heidelberg, Berlin, pp 89–99. doi:10.1007/978-3-642-23541-2_11

    Chapter  Google Scholar 

  27. Ibrahim I, Ibrahim Z, Ahmad H, Jusof MFM, Yusof ZM, Nawawi SW, Mubin M (2015) An assembly sequence planning approach with a rule-based multi-state gravitational search algorithm. Int J Adv Manuf Technol 79(5–8):1363–1376. doi:10.1007/s00170-015-6857-0

    Article  Google Scholar 

  28. Liu X, Liu Y, Xu B (2013) A converse method-based approach for assembly sequence planning with assembly tool. Int J Adv Manuf Technol 69(5–8):1359–1371

    Article  Google Scholar 

  29. Perrard C, Bonjour E (2013) Unification of the a priori inconsistencies checking among assembly constraints in assembly sequence planning. Int J Adv Manuf Technol 69(1–4):669–685

    Article  Google Scholar 

  30. Qu S, Jiang Z, Tao N (2013) An integrated method for block assembly sequence planning in shipbuilding. Int J Adv Manuf Technol 69(5–8):1123–1135

    Article  Google Scholar 

  31. Trigui M, BenHadj R, Aifaoui N (2015) An interoperability CAD assembly sequence plan approach. Int J Adv Manuf Technol 79(9):1465–1476

  32. Song Y, Song J, Cheng Z (2014) Assembly sequence planning for products with enclosed shell. TELKOMNIKA Indones J Electr Eng 12(8):6403–6410

    Google Scholar 

  33. Zeng B, Li M, Zhang Y (2013) Research on assembly sequence planning based on firefly algorithm. Chin J Mech Eng Cn 49(11):177–184, In Chinese

    Article  Google Scholar 

  34. Li M, Zhang Y, Zeng B, Zhou H, Liu J (2015) The modified firefly algorithm considering fireflies’ visual range and its application in assembly sequences planning. Int J Adv Manuf Technol:1–23;doi: 10.1007/s00170-015-7457-8

  35. Tseng H-E, Li R-K (1999) A novel means of generating assembly sequences using the connector concept. J Intell Manuf 10(5):423–435. doi:10.1023/a:1008971030395

    Article  Google Scholar 

  36. Wang L, Hou Y, Li X, Sun S (2013) An enhanced harmony search algorithm for assembly sequence planning. Int J Model Identif Control 18(1):18–25

    Article  Google Scholar 

  37. Tizhoosh HR (2005) Opposition-based learning: a new scheme for machine intelligence. CIMCA, Vienna, pp 695–701. doi:10.1109/CIMCA.2005.1631345

    Google Scholar 

  38. Zhao PJ (2010) A hybrid harmony search algorithm for numerical optimization. In: Computational Aspects of Social Networks (CASoN), International Conference on. IEEE, pp 255–258. doi: 10.1109/CASoN.2010.65

  39. Wang C-M, Huang Y-F (2010) Self-adaptive harmony search algorithm for optimization. Expert Syst Appl 37(4):2826–2837

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. The State Key Laboratory of Digital Manufacturing Equipment and Technology, Huazhong University of Science and Technology, Wuhan, 430074, China

    Xinyu Li, Kai Qin, Bing Zeng & Liang Gao

  2. ExxonMobil (China) Investment Co. Ltd., Chengdu, 610041, China

    Jiezhi Su

Authors
  1. Xinyu Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Kai Qin
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Bing Zeng
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Liang Gao
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Jiezhi Su
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Bing Zeng.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Li, X., Qin, K., Zeng, B. et al. Assembly sequence planning based on an improved harmony search algorithm. Int J Adv Manuf Technol 84, 2367–2380 (2016). https://doi.org/10.1007/s00170-015-7873-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00170-015-7873-9

Keywords

Search

Navigation