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Optimization of Assembly Sequence Planning Using Soft Computing Approaches: A Review

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Abstract

Assembly sequence planning (ASP) is an NP-hard problem that involves finding the most optimum sequence to assemble a product. The potential assembly sequences are too large to be handled effectively using traditional approaches for the complex mechanical product. Because of the problem complexity, ASP optimization is required for the efficient computational approach to determine the best assembly sequence. This topic has attracted many researchers from the computer science, engineering, and mathematics background. This paper presents a review of the research that used soft computing approaches to solve and optimize ASP problem. The review on this topic is important for the future researchers to contribute in ASP. The literature review was conducted through finding related published research papers specifically on ASP that used soft computing approaches. This review focused on ASP modeling approach, optimization algorithms and optimization objectives. Based on the conducted review, several future research directions were drawn. In terms of the problem modeling, future research should emphasize to model the flexible part in ASP. Besides, the consideration of sustainable manufacturing and ergonomic factors in ASP will also be the new directions in ASP research. In addition, a further study on new optimization algorithms is also suggested to obtain an optimal solution in reasonable computational time.

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References

  1. Youhui L, Xinhua L, Qi L (2012) Assembly sequence planning based on ant colony algorithm. Future Commun Comput Control Manag 141(51105069):397–404

    Article  Google Scholar 

  2. Hsu HY, Lin GCI (2002) Quantitative measurement of component accessibility and product assemblability for design for assembly application. Robot Comput Integr Manuf 18(1):13–27

    Article  Google Scholar 

  3. 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

    Google Scholar 

  4. Lu C, Yang Z (2016) Integrated assembly sequence planning and assembly line balancing with ant colony optimization approach. Int J Adv Manuf Technol 83(1):243–256

    Article  Google Scholar 

  5. Wang Y, Liu JH (2010) Chaotic particle swarm optimization for assembly sequence planning. Robot Comput Integr Manuf 26(2):212–222

    Article  Google Scholar 

  6. Kumar ER, Annamalai K (2015) Recent nontraditional optimization techniques for Assembly sequence planning. Int J Pure Appl Math 101(5):707–715

    Google Scholar 

  7. 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

    Google Scholar 

  8. Rashid MFF, Hutabarat W, Tiwari A (2011) 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

    Google Scholar 

  9. Marian RM, Luong LHS, Abhary K (2006) A genetic algorithm for the optimisation of assembly sequences. Comput Ind Eng 50(4):503–527

    Article  Google Scholar 

  10. Ghandi S, Masehian E (2015) Assembly sequence planning of rigid and flexible parts. J Manuf Syst 36:128–146

    Article  Google Scholar 

  11. Choi Y-K, Lee DM, Bin Cho Y (2008) An approach to multi-criteria assembly sequence planning using genetic algorithms”. Int J Adv Manuf Technol 42(1–2):180–188

    Google Scholar 

  12. Li M, Wu B, Hu Y, Jin C, Shi T (2013) A hybrid assembly sequence planning approach based on discrete particle swarm optimization and evolutionary direction operation. Int J Adv Manuf Technol 68(1–4):617–630

    Article  Google Scholar 

  13. Wang H, Rong Y, Xiang D (2014) Mechanical assembly planning using ant colony optimization. Comput Des 47:59–71

    Google Scholar 

  14. Guo J, Sun Z, Tang H, Yin L, Zhang Z (2015) Improved cat swarm optimization algorithm for assembly sequence planning. Open Autom Control Syst J 7(1):792–799

    Article  Google Scholar 

  15. Suszyński M, Żurek J, Legutko S (2014) Modelling of assembly sequences using hypergraph and directed graph. Tech Gaz 3651:1229–1233

    Google Scholar 

  16. 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 

  17. 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

    Article  Google Scholar 

  18. Rashid MFF, Tiwari A, Hutabarat W (2011) An integrated representation scheme for assembly sequence planning and assembly line balancing. In: Proceedings of the 9th International Conference on Manufacturing Research ICMR 2011, September, pp 125–131

  19. Ab Rashid MFF (2017) A hybrid Ant-Wolf algorithm to optimize assembly sequence planning problem. Assem Autom 37(2):238–248

    Article  Google Scholar 

  20. Tseng H-E, Tang C-E (2006) A sequential consideration for assembly sequence planning and assembly line balancing using the connector concept. Int J Prod Res 44(1):97–116

    Article  Google Scholar 

  21. Su YY, Liang D, Dong H (2014) Connector structure-based modeling of assembly sequence planning. Appl Mech Mater 496–500:2729–2732

    Article  Google Scholar 

  22. Chen S-F, Liu Y-J (2001) An adaptive genetic assembly-sequence planner. Int J Comput Integr Manuf 14(5):489–500

    Article  Google Scholar 

  23. Liu SL, Tang M, Dong JX (2003) Solving geometric constraints with genetic simulated annealing algorithm. J Zhejiang Univ Sci 4(5):532–541

    Article  Google Scholar 

  24. Su Q, Lai S-J (2010) 3D geometric constraint analysis and its application on the spatial assembly sequence planning. Int J Prod Res 48(5):1395–1414

    Article  MATH  Google Scholar 

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

    Article  Google Scholar 

  26. Yuan X (2002) An interactive approach of assembly planning. IEEE Trans Syst Man Cybern Part A Syst Hum 32(4):522–526

    Article  Google Scholar 

  27. Zhang J, Sun J, He Q (2010) An approach to assembly sequence planning using ant colony optimization. In: 2010 International conference on intelligent control and information processing, August, pp 230-233

  28. Selvanayaki K, Kalugasalam P (2013) Intelligent brain tumor tissue segmentation from magnetic resonance image (mri) using meta heuristic algorithms. J Glob Res Comput Sci 4(2):13–20

    Google Scholar 

  29. 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

    Article  Google Scholar 

  30. Kaur S, Agarwal P, Rana RS (2011) Ant colony optimization: a technique used for image processing. Int J Comput Sci Technol 2(2):173–175

    Google Scholar 

  31. Shan H, Zhou S, Sun Z (2009) Research on assembly sequence planning based on genetic simulated annealing algorithm and ant colony optimization algorithm. Assem Autom 29(3):249–256

    Article  Google Scholar 

  32. Biswal BB, Pattanayak SK, Mohapatra RN, Parida PK, Jha P (2012) Imece2012-87374 generation of optimized robotic assembly sequence using immune. In: Proceedings of ASME 2012 international mechanical engineering congress and exposition, pp 1–9

  33. Chen W-C, Tai P-H, Deng W-J, Hsieh L-F (2008) A three-stage integrated approach for assembly sequence planning using neural networks. Expert Syst Appl 34(3):1777–1786

    Article  Google Scholar 

  34. Tseng H-EHE, Wang W-PWP, Shih H-YHY (2007) Using memetic algorithms with guided local search to solve assembly sequence planning. Expert Syst Appl 33(2):451–467

    Article  Google Scholar 

  35. Martí R, Laguna M, Glover F (2006) Principles of scatter search. Eur J Oper Res 169(2):359–372

    Article  MathSciNet  MATH  Google Scholar 

  36. 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

    Google Scholar 

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

    Article  Google Scholar 

  38. Hui L, Jingxiao Z, Lieyan R, Zhen S (2013) Scheduling optimization of construction engineering based on ant colony optimized hybrid genetic algorithm. J Netw 8(6):1411–1416

    Google Scholar 

  39. Simons C, Smith J (2016) Exploiting antipheromone in ant colony optimisation for interactive searchbased software design and refactoring. In Proceedings of the 2016 on genetic and evolutionary computation conference companion, Denver, Colorado, USA, July 20–24, 2016, pp 143–144

  40. Sivakumar P, Elakia K (2016) A survey of ant colony optimization. Int J Adv Res Comput Sci Softw Eng 6(3):574–578

    Google Scholar 

  41. Ping Duan YA (2016) Research on an improved ant colony optimization algorithm and its application. Int J Hybrid Inf Technol 9(4):223–234

    Article  Google Scholar 

  42. Yan C, Zhunxia L, Sen C (2015) A fixture assembly sequence planning method based on ant colony algorithm. In: International industrial informatics and computer engineering conference (IIICEC 2015), pp 559–562

  43. Yang Z, Lu C, Zhao HW (2013) An ant colony algorithm for integrating assembly sequence planning and assembly line balancing. Appl Mech Mater 397–400:2570–2573

    Article  Google Scholar 

  44. Shakerian R, Kamali SH, Hedayati M, Alipour M (2011) Comparative study of ant colony optimization and particle swarm optimization for grid scheduling. J Math Comput Sci 3(3):469–474

    Article  Google Scholar 

  45. Selvi V, Umarani R (2010) Comparative analysis of ant colony and particle swarm optimization techniques. Int J Comput Appl 5(4):975–8887

    Google Scholar 

  46. Lu C, Huang HZ, Fuh JYH, Wong YS (2008) A multi-objective disassembly planning approach with ant colony optimization algorithm. Proc Inst Mech Eng Part B J Eng Manuf 222(11):1465–1474

    Article  Google Scholar 

  47. Shuang B, Chen J, Li Z (2007) Microrobot based micro-assembly sequence planning with hybrid ant colony algorithm. Int J Adv Manuf Technol 38(11–12):1227–1235

    Google Scholar 

  48. Wang JF, Liu JH, Zhong YF (2004) A novel ant colony algorithm for assembly sequence planning. Int J Adv Manuf Technol 25(11–12):1137–1143

    Google Scholar 

  49. Guntsch M, Middendort M, Schmeck H, Middendorf M, Schmeck H (2001) An ant colony optimization approach to dynamic TSP. In: Conference on genetic and evolutionary computation, pp 860–867

  50. Soltani M, Panichella A, Van Deursen A (2017) A guided genetic algorithm for automated crash reproduction. In Proceedings of the 39th international conference on software engineering, Buenos Aires, Argentina, May 20–28, 2017, pp 209–220

  51. Yuan W, Guan D (2017) Optimized trust-aware recommender system using genetic algorithm. Neural Netw World 27(1):77–94

    Article  Google Scholar 

  52. Sangwan KS, Kant G (2017) ScienceDirect optimization of machining parameters for improving energy efficiency using integrated response surface methodology and genetic algorithm approach. Procedia CIRP 00:517–522

    Article  Google Scholar 

  53. Hong T, Peng Y, Lin W, Wang S (2017) Empirical comparison of level-wise hierarchical multi-population genetic algorithm. J Inf Telecommun 1(1):66–78

    Google Scholar 

  54. Wang X, Zhang H, Chen Y, Liu Y (2017) Study of thermal sensitive point simulation and cutting trial of five axis machine tool based on genetic algorithm. Procedia Eng 174:550–556

    Article  Google Scholar 

  55. Mahmoodabadi MJ, Nemati AR (2016) A novel adaptive genetic algorithm for global optimization of mathematical test functions and real-world problems. Eng Sci Technol Int J 19(4):2002–2021

    Article  Google Scholar 

  56. Aibinu AM, Bello Salau H, Rahman NA, Nwohu MN, Akachukwu CM (2016) A novel clustering based genetic algorithm for route optimization. Eng Sci Technol Int J 19(4):2022–2034

    Article  Google Scholar 

  57. Liu Y, Dong H, Lohse N, Petrovic S (2016) A multi-objective genetic algorithm for optimisation of energy consumption and shop floor production performance. Int J Prod Econ 179:259–272

    Article  Google Scholar 

  58. Arunkumar G, Gnanambal I, Naresh S, Karthik PC, Patra JK (2016) Parameter optimization of three phase boost inverter using genetic algorithm for linear loads. Energy Procedia 90:559–565

    Article  Google Scholar 

  59. Magnor D, Sauer DU (2016) Optimization of PV battery systems using genetic algorithms. Energy Procedia 99(March):332–340

    Article  Google Scholar 

  60. Mathew AT, Rao CSP (2014) Implementation of genetic algorithm to optimize the assembly sequence plan based on penalty function. ARPN J Eng Appl Sci 9(4):453–456

    Google Scholar 

  61. Yasin A, Puteh N (2010) Product assembly sequence optimization based on genetic algorithm. Int J Comupt Sci Eng 02(09):3065–3070

    Google Scholar 

  62. Garg P (2009) A Comparison between memetic algorithm and genetic algorithm for the cryptanalysis of simplified data encryption standard algorithm. Int J Netw Secur Appl 1(1):34–42

    Google Scholar 

  63. Wang JF, Kang WL, Zhao JL, Chu KY (2016) A simulation approach to the process planning problem using a modified particle swarm optimization. Adv Prod Eng Manag 11(2):77–92

    Google Scholar 

  64. Kumar S, Sahu A, Pattnaik S (2015) Neuro structure optimization using adaptive particle swarm optimization. Procedia: Procedia Comput Sci 48:802–808

    Google Scholar 

  65. Ibrahim I, Ahmad H, Ibrahim Z, Mat Jusoh MF, Yusof ZM, Nawawi SW, Khalil K, Rahim MAA (2014) Multi-state particle swarm optimization for discrete combinatorial optimization problem. Int J Simul Syst Sci Technol 15(1):15–25

    Google Scholar 

  66. Mukred JAA, Ibrahim Z, Ibrahim I, Adam A, Wan K, Yusof ZM, Mokhtar N (2012) A binary particle swarm optimization approach to optimize assembly sequence planning. Adv Sci Lett 13:732–738

    Article  Google Scholar 

  67. Huang FY, Tseng YJ (2011) An integrated design evaluation and assembly sequence planning model using a particle swarm optimization approach. World Acad Sci Eng Technol 77:416–421

    Google Scholar 

  68. Tseng Y-J, Chen J-Y, Huang F-Y (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

    Article  MATH  Google Scholar 

  69. 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 

  70. Yu H, Yu JP, Zhang WL (2009) An particle swarm optimization approach for assembly sequence planning. Appl Mech Mater 16–19:1228–1232

    Article  Google Scholar 

  71. Cui H, Turan O (2009) Application of a new multi-agent based Particle Swarm Optimisation methodology in ship design. Comput Des 42:1–15

    Article  Google Scholar 

  72. Liu J, Wang Y, Gu Z (2008) Generation of optimal assembly sequences using particle swarm optimization, pp 1–8

  73. Li JG, Yao YX, Gao D, Liu CQ, Yuan ZJ (2008) Cutting parameters optimization by using particle swarm optimization (PSO). Appl Mech Mater 12:879–883

    Google Scholar 

  74. Barlow E, Tanyimboh TT (2014) Multiobjective memetic algorithm applied to the optimisation of water distribution systems. Water Resour Manag 28(8):2229–2242

    Article  Google Scholar 

  75. Gendreau M, Potvin JY (2010) Handbook of metaheuristics (International Series in Operations Research & Management Science), 2nd edn. Springer, New York, USA, ISBN: 978-1-4419-1665-5

  76. 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

    Google Scholar 

  77. Chang CCC-C, Tseng H-EHE, Meng LPL-P (2009) Artificial immune systems for assembly sequence planning exploration. Eng Appl Artif Intell 22(8):1218–1232

    Article  Google Scholar 

  78. Cao P-B, Xiao R-B (2006) Assembly planning using a novel immune approach. Int J Adv Manuf Technol 31(7–8):770–782

    Article  Google Scholar 

  79. Lin C-J (2002) A comparison of methods for multiclass support vector machines. IEEE Trans Neural Netw 13(2):1026–1027

    Google Scholar 

  80. Li J-Y, Lu C (2016) Assembly sequence planning with fireworks algorithm. Int J Model Optim 6(3):195–198

    Article  MathSciNet  Google Scholar 

  81. Mishra A, Deb S (2016) Assembly sequence optimization using a flower pollination algorithmbased approach. J Intell Manuf. https://doi.org/10.1007/s10845-016-1261-7

    Article  Google Scholar 

  82. Hui C, Yuan L, Kai-fu Z (2008) Efficient method of assembly sequence planning based on GAAA and optimizing by assembly path feedback for complex product. Int J Adv Manuf Technol 42(11–12):1187–1204

    Google Scholar 

  83. Meng Y (2016) An application of the geometric method to assembly sequence planning. Int J Simul Syst Sci Technol 17(19):17.1–17.4

  84. Mishra A, Deb S (2016) An intelligent methodology for assembly tools selection and assembly sequence optimisation. In: Mandal DK, Syan CS (eds) CAD/CAM, robotics and factories of the future. Springer India, New Delhi, pp 323–333

    Chapter  Google Scholar 

  85. Lu C, Wong YS, Fuh JYH (2006) An enhanced assembly planning approach using a multi-objective genetic algorithm. Proc Inst Mech Eng Part B J Eng Manuf 220(2):255–272

    Article  Google Scholar 

  86. Lv H, Lu C (2009) A discrete particle swarm optimization algorithm for assembly sequence planning. In: 2009 8th international conference on reliability, maintainability and safety, pp 1119–1122

  87. Ding Z, Hon B (2013) Constraints analysis and evaluation of manual assembly. CIRP Ann Manuf Technol 62(1):1–4

    Article  Google Scholar 

  88. Mirjalili S, Mirjalili SM, Lewis A (2014) Grey Wolf Optimizer. Adv Eng Softw 69:46–61

    Article  Google Scholar 

  89. Mirjalili S, Jangir P, Saremi S (2016) Multi-objective ant lion optimizer: a multi-objective optimization algorithm for solving engineering problems. Appl Intell 46(1): 79–95

    Article  Google Scholar 

  90. Mirjalili S (2015) Moth-flame optimization algorithm: a novel nature-inspired heuristic paradigm. Knowledge-Based Syst 89:228–249

    Article  Google Scholar 

  91. Mirjalili S (2016) SCA: a sine cosine algorithm for solving optimization problems. Knowledge-Based Syst 96:120–133

    Article  Google Scholar 

  92. Mirjalili S, Lewis A (2016) The whale optimization algorithm. Adv Eng Softw 95:51–67

    Article  Google Scholar 

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Funding

This study was funded by the research grant from Universiti Malaysia Pahang.

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  1. Faculty of Mechanical Engineering, Universiti Malaysia Pahang, 26600, Pekan, Pahang, Malaysia

    Muhammad Arif Abdullah, Mohd Fadzil Faisae Ab Rashid & Zakri Ghazalli

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  1. Muhammad Arif Abdullah
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Correspondence to Mohd Fadzil Faisae Ab Rashid.

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Abdullah, M.A., Ab Rashid, M.F.F. & Ghazalli, Z. Optimization of Assembly Sequence Planning Using Soft Computing Approaches: A Review. Arch Computat Methods Eng 26, 461–474 (2019). https://doi.org/10.1007/s11831-018-9250-y

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