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A dynamic parameter controlled harmony search algorithm for assembly sequence planning

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Abstract

Assembly sequence planning (ASP) plays an important role in intelligent manufacturing. As ASP is a non-deterministic polynomial (NP) hard problem, it is scarcely possible for a brute force approach to find out the optimal solution. Therefore, increasing meta-heuristic algorithms are introduced to solve the ASP problem. However, due to the discreteness and strong constraints of ASP problem, most meta-heuristics are unsuitable or inefficient to optimize it. Harmony search (HS) algorithm is one of the most suitable meta-heuristics for solving the problem. This paper proposes a dynamic parameter controlled harmony search (DPCHS) for solving ASP problems including a transformation of the assembly sequences by the largest position value (LPV) rule, initializing harmony memory with opposition-based learning (OBL) and designing dynamic parameters to control evolution. The key improvement to former work lies in the introduction of a dynamic pitch adjusting rate and bandwidth, which are adapting their value during the evolution. The performances of the DPCHS and the fixed harmony search algorithm are compared thoroughly in the case studies. Meantime, the efficiency of this algorithm in solving ASP problems is tested using two cases, and the results of other popular algorithms are compared. Furthermore, the DPCHS has been successfully applied to an industrial ASP problem.

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References

  1. Shoval S, Efatmaneshnik M, Ryan MJ (2016) Assembly sequence planning for processes with heterogeneous reliabilities. Int J Prod Res 1–23. doi:10.1080/00207543.2016.1213449

  2. Jiménez P (2011) Survey on assembly sequencing: a combinatorial and geometrical perspective. J Intell Manuf 24(2):235–250. doi:10.1007/s10845-011-0578-5

    Article  Google Scholar 

  3. Yin ZP, Ding H, Li HX, Xiong YL (2003) A connector-based hierarchical approach to assembly sequence planning for mechanical assemblies. Comput Aided Des 35(1):37–56

    Article  Google Scholar 

  4. Zhang Z, Yuan B, Zhang Z (2016) A new discrete double-population firefly algorithm for assembly sequence planning. P I MECH ENG B-J ENG 230(12):2229–2238

    Google Scholar 

  5. 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 

  6. Gao KZ, Suganthan PN, Pan QK, Chua TJ, Cai TX, Chong CS (2014) Discrete harmony search algorithm for flexible job shop scheduling problem with multiple objectives. J Intell Manuf 27(2):363–374. doi:10.1007/s10845-014-0869-8

    Article  MATH  Google Scholar 

  7. Zhang ZY, Li Z, Jiang ZB (2008) Computer-aided block assembly process planning in shipbuilding based on rule-reasoning. Chin J Mech Eng 21(2):99–103. doi:10.3901/Cjme.2008.02.099

    Article  Google Scholar 

  8. De Mello LH, Sanderson AC (1990) AND/OR graph representation of assembly plans. IEEE Trans Robot Autom 6(2):188–199

    Article  Google Scholar 

  9. Wan W, Harada K (2016) Integrated assembly and motion planning using regrasp graphs. Robot Biomim 3(1):18. doi:10.1186/s40638-016-0050-2

    Article  Google Scholar 

  10. Xin L, Jianzhong S, Yujun C (2016) An efficient method of automatic assembly sequence planning for aerospace industry based on genetic algorithm. Int J Adv Manuf Technol 1–9. doi:10.1007/s00170-016-9449-8

  11. Kucukkoc I, Buyukozkan K, Satoglu SI, Zhang DZ (2015) A mathematical model and artificial bee colony algorithm for the lexicographic bottleneck mixed-model assembly line balancing problem. J Intell Manuf 1–13. doi:10.1007/s10845-015-1150-5

  12. Saif U, Guan ZL, Zhang L, Mirza J, Lei Y (2016) Hybrid Pareto artificial bee colony algorithm for assembly line balancing with task time variations. Int J Comput Integr Manuf 30(2–3):255–270. doi:10.1080/0951192x.2016.1145802

    Google Scholar 

  13. Zhang W, Ma M, Li H, Yu J (2016) Generating interference matrices for automatic assembly sequence planning. Int J Adv Manuf Technol 1–15. doi:10.1007/s00170-016-9410-x

  14. Belhadj I, Trigui M, Benamara A (2016) Subassembly generation algorithm from a CAD model. Int J Adv Manuf Technol 87(9–12):2829–2840. doi:10.1007/s00170-016-8637-x

    Article  Google Scholar 

  15. 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 

  16. Che ZH (2010) A genetic algorithm-based model for solving multi-period supplier selection problem with assembly sequence. Int J Prod Res 48(15):4355–4377. doi:10.1080/00207540903049399

    Article  MATH  Google Scholar 

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

    Article  Google Scholar 

  18. Xing YF, Wang YS (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 

  19. Lv HG, 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. doi:10.1007/s00170-010-2519-4

    Article  Google Scholar 

  20. Tseng YJ, Chen JY, Huang FY (2010) A particle swarm optimisation algorithm for multi-plant assembly sequence planning with integrated assembly sequence planning and plant assignment. Int J Prod Res 48(10):2765–2791. doi:10.1080/00207540902791835

    Article  MATH  Google Scholar 

  21. 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  MathSciNet  Google Scholar 

  22. Wang D, Shao X, Liu S (2016) Assembly sequence planning for reflector panels based on genetic algorithm and ant colony optimization. Int J Adv Manuf Technol 1–11. doi:10.1007/s00170-016-9822-7

  23. Zhang HY, Liu HJ, Li LY (2014) 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

    Article  Google Scholar 

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

    Article  Google Scholar 

  25. Tseng HE, Wang WP, Shih HY (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 

  26. Gao L, Qian W, Li X, 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 

  27. Li X, Qin K, Zeng B, Gao L, Su J (2015) Assembly sequence planning based on an improved harmony search algorithm. Int J Adv Manuf Technol 84(9–12):2367–2380. doi:10.1007/s00170-015-7873-9

    Google Scholar 

  28. Geem ZW, Kim JH, Loganathan GV (2001) A new heuristic optimization algorithm: harmony search. SIMULATION 76(2):60–68

    Article  Google Scholar 

  29. Kong Z, Wang L, Jia W (2015) Approximate normal parameter reduction of fuzzy soft set based on harmony search algorithm. In: BDCloud 2015. IEEE, pp 321–324. doi: 10.1109/BDCloud.2015.63

  30. Li YZ, Li XP, Gupta JND (2015) Solving the multi-objective flowline manufacturing cell scheduling problem by hybrid harmony search. Expert Syst Appl 42(3):1409–1417. doi:10.1016/j.eswa.2014.09.007

    Article  Google Scholar 

  31. Kong XY, Gao LQ, Ouyang HB, Li S (2015) Solving large-scale multidimensional knapsack problems with a new binary harmony search algorithm. Comput Oper Res 63:7–22. doi:10.1016/j.cor.2015.04.018

    Article  MathSciNet  MATH  Google Scholar 

  32. Geem ZW (2016) Artificial satellite heat pipe design using harmony search. In: Harmony Search Algorithm. Springer, Verlag Berlin Heidelberg, pp 423–433

    Chapter  Google Scholar 

  33. 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. doi:10.1504/IJMIC.2013.051929

    Article  Google Scholar 

  34. Whitney DE (2004) Mechanical assemblies: their design, manufacture, and role in product development, vol 22. Oxford university press, New York

    Google Scholar 

  35. Sinanoglu C, Borklu HR (2005) An assembly sequence-planning system for mechanical parts using neural network. Assem Autom 25(1):38–52. doi:10.1108/01445150510578996

    Article  Google Scholar 

  36. 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 82(5–8):1381–1403. doi:10.1007/s00170-015-7457-8

    Google Scholar 

  37. Pan QK, Tasgetiren MF, Liang YC (2008) A discrete particle swarm optimization algorithm for the no-wait flowshop scheduling problem. Comput Oper Res 35(9):2807–2839. doi:10.1016/j.cor.2006.12.030

    Article  MathSciNet  MATH  Google Scholar 

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

    Google Scholar 

  39. Zhao P-J (2010) A hybrid harmony search algorithm for numerical optimization. In: CASoN, 2010. pp 255–258. doi: 10.1109/CASoN.2010.65

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Correspondence to Bing Zeng.

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Li, X., Qin, K., Zeng, B. et al. A dynamic parameter controlled harmony search algorithm for assembly sequence planning. Int J Adv Manuf Technol 92, 3399–3411 (2017). https://doi.org/10.1007/s00170-017-0352-8

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