Abstract
The main issue for enhancing the productivity in Printed Circuit Board (PCB) is to reduce the cycle time for pick and place (PAP) operations; i.e., to minimize the time for the PAP operations. According to the characteristics of the PAP problems, the sequence for the placement of components can be mostly treated as the Travelling Salesman Problem (TSP). In this paper, a Genetic Algorithm (GA) with External Self-evolving Multiple Archives (ESMA) is developed for minimizing the PAP operations in PCB assembly line. ESMA focuses on the issue of improving the premature convergence time in GA by adopting efficient measures for population diversity, effective diversity control and mutation strategies to enhance the global searching ability. Three mechanisms for varietal GA such as Clustering Strategy, Switchable Mutation and Elitist Propagation have been designed based on the concept of increasing the dynamic diversity of the population. The experimental results in PCB and TSP instances show that the proposed approach is very promising and it contains the ability of local and global searching. The experimental results show ESMA can further improve the performance of GA by searching the solution space with more promising results.
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Ahmadi J., Grotzinger S., Johnson D. (1988) Component allocation and partitioning for a dual delivery placement machine. Operations Research 36: 176–191
Ahmadi R. H., Mamer J. W. (1999) Routing heuristics for automated pick and place machines. European Journal of Operational Research 117: 533–552
Altinkemer K., Kazaz B., Koksalan M., Moskowitz H. (2000) Optimization of printed circuit board manufacturing: Integrated modeling and algorithms. European Journal of Operational Research 124: 409–421
Ball M. O., Magazine M. J. (1988) Sequencing of insertions in printed circuit board assembly. Operations Research 36(2): 192–201
Bäck T., Fogel D. B., Michalewicz Z. (1997) Handbook of evolutionary computation. IOP Publishing Ltd and Oxford University Press, Oxford
Bossert W., Pattanaik P. K., Xu Y. (2003) Similarity of options and the measurement of diversity. Journal of Theoretical Politics 15: 405–421
Chang D. S., Kuo Y. C., Chen T. Y. (2008) Productivity measurement of the manufacturing process for outsourcing decisions: The case of a Taiwanese printed circuit board manufacturer. International Journal of Production Research 46(24): 6981–6995
Chang, P. C., Huang, W. H., Liu, J. Y. C., Chen, C., & Ting, C. J. (2008a). Dynamic diversity control by injecting artificial chromosomes for solving TSP problems. In 2008 IEEE congress on evolutionary computation (CEC 2008), Hong Kong (pp. 542–549).
Chang, P. C., Huang, W. H., Ting, C. J., & Fan, C. Y. (2008b). Dynamic diversity control in genetic algorithm for extended exploration of solution space in multi-objective TSP. In Innovative computing information and control, 2008. ICICIC ‘08. 3rd International conference, Dalian (pp. 461–464).
Chang P. C., Hsieh J. C., Chen S. H., Lin J. L., Huang W. H. (2009) Artificial chromosomes embedded in genetic algorithm for a chip resistor scheduling problem in minimizing the makespan. Expert Systems with Applications 36((3)Part 2): 7135–7141
Chang P. C., Chen S. H. (2009) The development of a sub-population genetic algorithm II (SPGAII) for the multi-objective combinatorial problems. Applied Soft Computing Journal 9(1): 173–181
Chang P. C., Liu C. H., Lai Robert K. (2008) A fuzzy case-based reasoning model for sales forecasting in print circuit board industries. Expert Systems with Applications 34(3): 2049–2058
Chang P. C., Hsieh J. C., Wang C. Y. (2007) Adaptive multi-objective genetic algorithms for scheduling to drilling operation of printed circuit board industry. Applied Soft Computing Journal 7(3): 800–806
Chang Pei-Chann, Hsieh J. C., Liao T. Warren (2005) Evolving fuzzy rules for due-date assignment problem in semiconductor manufacturing factory. Journal of Intelligent Manufacturing 16(5): 549–557
Chun J. S., Jung H. K., Hahn S. Y. (1998) A study on comparison of optimization performances between immune algorithm and other heuristic algorithms. IEEE Transactions on Magnetics 34(5): 2972–2975
De Souza R., Lijun W. (1995) Intelligent optimization of component onsertion in multi-head concurrent operation PCB machines. Journal of Intelligent Manufacturing 6(4): 235–243
Duman E., Or I. (2004) Precedence constrained TSP arising in printed circuit board assembly. International Journal of Production Research 42(1): 67–78
Eshelman L. I. (1991) The CHC adaptive search algorithm: How to have safe search when engaging in nontraditional genetic recombination. In: Rawlins G. J. E. (ed.) Foundations of genetic algorithm l. Morgan Kanfmann, San Mateo, CA, pp 265–283
Flood M. M. (1956) The traveling-salesman problem. Operations Research 4: 61–75
Fonseca, C. M., & Fleming, P. J. (1995). Multi-objective genetic algorithms made easy: Selection, sharing and mating restriction. In Proceedings of the first international conference on generic algorithms in engineering systems: Innovations and applications, Sheffield, UK (pp. 45–52).
Francis R. L., Hamacher H. W., Lee C. Y., Yeralan S. (1994) Finding placement sequences and bin locations for Cartesian robots. IIE Transactions 26: 47–59
Fu H. P., Su C. T. (2000) A comparison of search techniques for minimizing assembly time in Printed Wiring Assembly. International Journal of Production Economics 63: 83–98
Gen M., Cheng R. (1997) Genetic algorithms and engineering design. Wiley, New York
Goldberg, D. E., & Richardson, J. (1987). Genetic algorithms with sharing for multimodal function optimization. In Genetic algorithms and their applications (ICGA’87) (pp. 41–49).
Grötschel, M. (2009). The travelling salesman problem and its applications. Institute of Mathematics, Technische Universität Berlin. Available from: http://www.zib.de/groetschel/index.html.
Gutin G., Punnen A. P. (2004) The traveling salesman problem and its variations (combinatorial optimization). Springer, New York
Ho W., Ji P. (2004) A hybrid genetic algorithm for component sequencing and feeder arrangement. Journal of Intelligent Manufacturing 15(3): 307–315
Ho W., Ji P. (2009) An integrated scheduling problem of PCB components on sequential pick-and-place machines: Mathematical models and heuristic solutions. Expert Systems with Applications 1(36): 7002–7010
Hop N. V. (2003) Board sequencing and component loading problem for a single machine in PCB assembly planning. International Journal of Production Research 41(18): 4299–4315
Kumar R., Li H. (1995) Integer programming approach to printed circuit board assembly time optimization. IEEE Transactions on Components, Packaging, and Manufacturing Technology- Part B 18: 720–727
Larrañaga P., Kuijpers C. M. H., Murga R. H., Inza I., Dizdarevic S. (1999) Genetic algorithms for the travelling salesman problem: A review of representations and operators. Artificial Intelligence Review 13(2): 129–170
Mori, N. Yoshida, J., Tamaki, H. Kita, H., & Nishikawa, Y. (1995). A thermo-dynamical selection rule for the genetic algorithm. In Proceedings of the second IEEE international conference on evolutionary computation, Perth, WA (pp. 188–192).
Sanchez J. M., Priest J. W. (1991) Optimal component-insertion sequenceplanning methodology for the semiautomatic assembly of printed circuit boards. Journal of Intelligent Manufacturing 2(3): 177–187
Shimodaira, H. (1996). A new genetic algorithm using large mutation rates and population-elitist selection (GALME). In Proceedings of the eighth IEEE international conference on tools with artificial intelligence, Toulouse, France (pp. 25–32).
Shimodaira, H. (1997). DCGA: A diversity control oriented genetic algorithm. In Proceedings of the second international conference on genetic algorithms in engineering systems: Innovations and applications, Glasgow, UK (pp. 444–449).
Shimodaira, H. (2001). A diversity-control-oriented genetic algorithm (DCGA): Performance in function optimization. In The 2001 congress on evolutionary computation, Seoul, Korea (vol. 1, pp. 44–51).
Tsai J. T., Ho W. H., Liu T. K., Chou J. H. (2007) Improved immune algorithm for global numerical optimization and job-shop scheduling problems. Applied Mathematics and Computation 194: 406–424
Tucker, A., & Flood, M. (1984). Seeley G. Mudd manuscript. Library web at the URL: http://www.princeton.edu/mudd/math, 11.
Turkcan A., Akturk M. S. (2003) A problem space genetic algorithm in multiobjective optimization. Journal of Intelligent Manufacturing 14(3–4): 363–378
Ursem, R. K., Angeline, P. J., Michalewicz, Z., Schoenauer, M., Yao, X., & Zalzala, A. (1999). Multinational evolutionary algorithms. In Proceedings of the congress of evolutionary computation (CEC-99) (vol. 3, pp. 1633–1640).
Wang W., Nelson P. C., Tirpak T. M. (1999) Optimization of high-speed multistation SMT placement machines using evolutionary algorithms. IEEE Transactions on Electronics Packaging Manufacturing 22(2): 137–146
Whitley, D. (1989). The GENITOR algorithm and selection pressure: Why rank-based allocation of reproduction trials is best. In Proceedings of the third international conference on genetic algorithms (pp. 116–121).
Yilmaz I. O., Grunow M., Gunther H. O. (2007) Development of group setup strategies for makespan minimisation in PCB assembly. International Journal of Production Research 45(4): 871–897
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Chang, PC., Huang, WH. & Ting, CJ. Developing a varietal GA with ESMA strategy for solving the pick and place problem in printed circuit board assembly line. J Intell Manuf 23, 1589–1602 (2012). https://doi.org/10.1007/s10845-010-0461-9
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DOI: https://doi.org/10.1007/s10845-010-0461-9