Ant colony optimization for assembly sequence planning based on parameters optimization

Abstract

As an important part of product design and manufacturing, assembly sequence planning (ASP) has a considerable impact on product quality and manufacturing costs. ASP is a typical NP-complete problem that requires effective methods to find the optimal or near-optimal assembly sequence. First, multiple assembly constraints and rules are incorporated into an assembly model. The assembly constraints and rules guarantee to obtain a reasonable assembly sequence. Second, an algorithm called SOS-ACO that combines symbiotic organisms search (SOS) and ant colony optimization (ACO) is proposed to calculate the optimal or near-optimal assembly sequence. Several of the ACO parameter values are given, and the remaining ones are adaptively optimized by SOS. Thus, the complexity of ACO parameter assignment is greatly reduced. Compared with the ACO algorithm, the hybrid SOS-ACO algorithm finds optimal or near-optimal assembly sequences in fewer iterations. SOS-ACO is also robust in identifying the best assembly sequence in nearly every experiment. Lastly, the performance of SOS-ACO when the given ACO parameters are changed is analyzed through experiments. Experimental results reveal that SOS-ACO has good adaptive capability to various values of given parameters and can achieve competitive solutions.

Graphical abstract

Keywords

assembly sequence planning / ant colony optimization / symbiotic organisms search / parameter optimization

References

[1] Boothroyd G. Product design for manufacture and assembly. Computer-Aided Design, 1994, 26(7): 505–520

[2] Lambert A J D, Gupta S M. Disassembly Modeling for Assembly, Maintenance, Reuse, and Recycling. Boca Raton: CRC Press, 2004

[3] Abdullah M A, Ab Rashid M F F, Ghazalli Z. Optimization of assembly sequence planning using soft computing approaches: A review. Archives of Computational Methods in Engineering, 2019, 26(2): 461–474

[4] Tseng Y J, Chen J Y, Huang F Y. A multi-plant assembly sequence planning model with integrated assembly sequence planning and plant assignment using GA. International Journal of Advanced Manufacturing Technology, 2010, 48(1–4): 333–345

[5] Hu X Y, Gao B. Research on assembly sequence planning based on fluid flow mechanism. In: Proceedings of IEEE International Conference of Intelligent Robotic and Control Engineering (IRCE). Lanzhou: IEEE, 2018, 46–50

[6] Wang J F, Li S Q, Liu J H, Computer aided assembly planning: A survey. Journal of Engineering Graphics, 2005, 2: 1–7 (in Chinese)

[7] Homem de Mello L S, Sanderson A C. A correct and complete algorithm for the generation of mechanical assembly sequences. IEEE Transactions on Robotics and Automation, 1991, 7(2): 228–240

[8] Chen R S, Lu K Y, Tai P H. Optimisation of assembly plan through a three-stage integrated approach. International Journal of Computer Applications in Technology, 2004, 19(1): 28–38

[9] Zha X F, Lim S Y E, Fok S C. Integrated knowledge-based Petri net intelligent flexible assembly planning. Journal of Intelligent Manufacturing, 1998, 9(3): 235–250

[10] Gunji A B, Deepak B B B V L, Bahubalendruni C M V A R, An optimal robotic assembly sequence planning by assembly subsets detection method using teaching learning-based optimization algorithm. IEEE Transactions on Automation Science and Engineering, 2018, 15(3): 1369–1385

[11] Jiménez P. Survey on assembly sequencing: A combinatorial and geometrical perspective. Journal of Intelligent Manufacturing, 2013, 24(2): 235–250

[12] Pandian R S, Kamalakannan R, Sivakumar P, Heuristic and meta-heuristic algorithms for solving medium and large scale sized cellular manufacturing system NP-hard problems: A comprehensive review. Materials Today: Proceedings, 2019, 21(Part 1): 66–72

[13] Huang Z Z, Zhuang Z L, Cao Q, A survey of intelligent algorithms for open shop scheduling problem. Procedia CIRP, 2019, 83: 569–574

[14] Li X, Shang J Z, Cao Y J. An efficient method of automatic assembly sequence planning for aerospace industry based on genetic algorithm. International Journal of Advanced Manufacturing Technology, 2017, 90(5–8): 1307–1315

[15] Qu X T, Zhang K, Wang X X, Hybrid cycle algorithm-based intelligent assembly sequence planning of complex assembly. Journal of Northeastern University (Natural Science), 2019, 40(12): 1767–1772 (in Chinese)

[16] Wang J F, Liu J H, Zhong Y F. A novel ant colony algorithm for assembly sequence planning. International Journal of Advanced Manufacturing Technology, 2005, 25(11–12): 1137–1143

[17] Wang H, Rong Y M, Xiang D. Mechanical assembly planning using ant colony optimization. Computer-Aided Design, 2014, 47: 59–71

[18] Yu J P, Wang C E. A max–min ant colony system for assembly sequence planning. International Journal of Advanced Manufacturing Technology, 2013, 67(9–12): 2819–2835

[19] Wu Y J, Cao Y, Wang Q F. Assembly sequence planning method based on particle swarm algorithm. Cluster Computing, 2019, 22(S1): 835–846

[20] Lv H G, Lu C. An assembly sequence planning approach with a discrete particle swarm optimization algorithm. International Journal of Advanced Manufacturing Technology, 2010, 50(5–8): 761–770

[21] Wang Y, Liu J H. Chaotic particle swarm optimization for assembly sequence planning. Robotics and Computer-Integrated Manufacturing, 2010, 26(2): 212–222

[22] Mishra A, Deb S. Assembly sequence optimization using a flower pollination algorithm-based approach. Journal of Intelligent Manu-facturing, 2019, 30(2): 461–482

[23] Bahubalendruni M V A R, Sudhakar U, Lakshmi K V V. Subassembly detection and optimal assembly sequence generation through elephant search algorithm. International Journal of Mathematical, Engineering and Management Sciences, 2019, 4(4): 998–1007

[24] Abdullah A, Ab Rashid M F F, Ponnambalam S G, Energy efficient modeling and optimization for assembly sequence planning using moth flame optimization. Assembly Automation, 2019, 39(2): 356–368

[25] Ab Rashid M F F. A hybrid ant-wolf algorithm to optimize assembly sequence planning problem. Assembly Automation, 2017, 37(2): 238–248

[26] Mirjalili S, Mirjalili S M, Lewis A. Grey wolf optimizer. Advances in Engineering Software, 2014, 69: 46–61

[27] Shuang B, Chen J P, Li Z B. Microrobot based micro-assembly sequence planning with hybrid ant colony algorithm. International Journal of Advanced Manufacturing Technology, 2008, 38(11–12): 1227–1235

[28] Wang Y, Tian D. A weighted assembly precedence graph for assembly sequence planning. International Journal of Advanced Manufacturing Technology, 2016, 83(1–4): 99–115

[29] Deng W, Xu J J, Song Y J, An effective improved co-evolution ant colony optimization algorithm with multi-strategies and its application. International Journal of Bio-Inspired Computation, 2020, 16(3): 158–170

[30] Luan W J, Liu G J, Jiang C J, MPTR: A maximal-marginal-relevance-based personalized trip recommendation method. IEEE Transactions on Intelligent Transportation Systems, 2018, 19(11): 3461–3474

[31] Boukens M, Boukabou A, Chadli M. A real time self-tuning motion controller for mobile robot systems. IEEE/CAA Journal of Automatica Sinica, 2019, 6(1): 84–96

[32] Peker M, Sen B, Kumru P Y. An efficient solving of the traveling salesman problem: The ant colony system having parameters optimized by the Taguchi method. Turkish Journal of Electrical Engineering and Computer Sciences, 2013, 21: 2015–2036

[33] Wang J J, Kumbasar T. Parameter optimization of interval Type-2 fuzzy neural networks based on PSO and BBBC methods. IEEE/CAA Journal of Automatica Sinica, 2019, 6(1): 247–257

[34] Gao S C, Zhou M C, Wang Y R, Dendritic neuron model with effective learning algorithms for classification, approximation, and prediction. IEEE Transactions on Neural Networks and Learning Systems, 2019, 30(2): 601–614

[35] Botee H M, Bonabeau E. Evolving ant colony optimization. Advances in Complex Systems, 1998, 1(02n03): 149–159

[36] Mahi M, Baykan O K, Kodaz H. A new hybrid method based on particle swarm optimization, ant colony optimization and 3-Opt algorithms for traveling salesman problem. Applied Soft Computing, 2015, 30: 484–490

[37] Ariyaratne M K A, Fernando T G I, Weerakoon S. A self-tuning firefly algorithm to tune the parameters of ant colony system. International Journal of Swarm Intelligence, 2018, 3(4): 309–331

[38] Cheng M Y, Prayogo D. Symbiotic organisms search: A new metaheuristic optimization algorithm. Computers & Structures, 2014, 139: 98–112

[39] Wang Y, Wu Y W, Xu N. Discrete symbiotic organism search with excellence coefficients and self-escape for traveling salesman problem. Computers & Industrial Engineering, 2019, 131: 269–281

[40] Panda A, Pani S. A symbiotic organisms search algorithm with adaptive penalty function to solve multi-objective constrained optimization problems. Applied Soft Computing, 2016, 46: 344–360

[41] Smith S F, Smith G C, Liao X. Automatic stable assembly sequence generation and evaluation. Journal of Manufacturing Systems, 2001, 20(4): 225–235

[42] Yin W S. Assembly design system based on engineering connection. Frontiers of Mechanical Engineering, 2016, 11(4): 423–432

[43] Lu C, Wong Y S, Fuh J Y H. An enhanced assembly planning approach using a multi-objective genetic algorithm. Proceedings of the Institution of Mechanical Engineers. Part B, Journal of Engineering Manufacture, 2006, 220(2): 255–272

[44] Bedeoui A, Hadj R B, Hammadi M, Assembly sequence plan generation of heavy machines based on the stability criterion. International Journal of Advanced Manufacturing Technology, 2019, 102(9–12): 2745–2755

[45] Feng Y X, Zhou M C, Tian G D, Target disassembly sequencing and scheme evaluation for CNC machine tools using improved multiobjective ant colony algorithm and fuzzy integral. IEEE Transactions on Systems, Man, and Cybernetics. Systems, 2019, 49(12): 2438–2451

[46] Hong Z X, Feng Y X, Li Z W, An integrated approach for multi-objective optimisation and MCDM of energy internet under uncertainty. Future Generation Computer Systems, 2019, 97: 90–104

[47] Feng Y X, Gao Y C, Tian G D, Flexible process planning and end-of-life decision-making for product recovery optimization based on hybrid disassembly. IEEE Transactions on Automation Science and Engineering, 2019, 16(1): 311–326

[48] Dorigo M, Gambardella L M. Ant colonies for the travelling salesman problem. Bio Systems, 1997, 43(2): 73–81

[49] Dorigo M, Caro G D, Gambardella L M. Ant algorithms for discrete optimization. Artificial Life, 1999, 5(2): 137–172

[50] Chan F T S, Swarnkar R. Ant colony optimization approach to a fuzzy goal programming model for a machine tool selection and operation allocation problem in an FMS. Robotics and Computer-Integrated Manufacturing, 2006, 22(4): 353–362

[51] Wang D, Shao X D, Liu S M. Assembly sequence planning for reflector panels based on genetic algorithm and ant colony optimization. International Journal of Advanced Manufacturing Technology, 2017, 91(1–4): 987–997

[52] Ha C H. Evolving ant colony system for large-sized integrated process planning and scheduling problem considering sequence-dependent setup times. Flexible Services and Manufacturing Journal, 2020, 32(3): 523–560

[53] McGovern S M, Gupta S M. Ant colony optimization for disassembly sequencing with multiple objectives. International Journal of Advanced Manufacturing Technology, 2006, 30(5–6): 481–496

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