Simulated Annealing Genetic Algorithm and its Application in Mixed-Model Assembly Line Design

Abstract:

Simple genetic algorithm has shortcomings of poor local search ability and premature convergence. To overcome these disadvantages, simulated annealing algorithm which has good local search ability was combined with genetic algorithm to form simulated annealing genetic algorithm. The tests by two commonly used test functions of Shaffer’s F6 and Rosenbrock show that simulated annealing genetic algorithm outperforms the simple genetic algorithm both in convergence rate and convergence quality. Finally, the simulated annealing genetic algorithm was firstly applied in a practical problem of balancing and sequencing design of mixed-model assembly line, once again, the solution results show that simulated annealing genetic algorithm outperforms the simple genetic algorithm. Meanwhile, it provides a new algorithm for solving the design problem of mixed-model assembly line.

• Periodical: Advanced Materials Research (Volume 136)
• Pages: 64-68
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.136.64
• Online since: October 2010
• Authors: Yan Jiang, Xiang Feng Li, Dun Wen Zuo, Guang Ming Jiao, Shan Liang Xue
• Keywords: Balancing, Genetic Algorithm (GA), Mixed-Model Assembly Line, Sequencing, Simulated Annealing Algorithm

References

[1] Holland J. H. Adaptation in nature and artificial systems[M]. Ann Arbor: The University of Michigan Press, (1975).

[2] Godberg D. E. Genetic algorithms in search, Optimization and Machine Learning[M]. Addison-Wesley, New York, (1989).

[3] Kirkpatrick S., Gellatt C. D., Vechi M. R. Optimization by simulated annealing[J]. Science, 1983, 220: 671-680.

[4] X. P. Wang, L. M. Cao. Genetic algorithm - theory, application and software implementation[M]. XiAn: XiAn JiaoTong University, (2005).

[5] Rosenbrock H. H. An automatic method for finding the greatest or least value of a function[J]. Computer Journal, 1960, 3(3): 175-184.

DOI: 10.1093/comjnl/3.3.175

[6] Y. J. Lei, S. W. Zhang, X. W. Li, etc. Genetic algorithm toolbox of Matlab and its application[M]. XiAn: Xi Dian University Press, 2005. Fig. 4 The convergence condition of the minimum value of objective function Fig. 5 The assembly sketch given by the program using SAGA.

[7] A. R. Rahimi-Vahed, M. Rabbani, R. Tavakkoli-Moghaddam, et al. A multi-objective scatter search for mixed-model assembly line sequencing problem[J]. Advanced Engineering Informatics, 2007, 21: 85-99.

DOI: 10.1016/j.aei.2006.09.007

[8] Y. H. Cui, D. W. Zuo, B. M. Shen, etc. Co-optimization for mixed-model assembly flow line to minimize the time of assembly[J]. Manufacturing Information Engineering of China, 2008, 37(23): 42-45.

[9] Chu Ju, Y.H. Kim, Yeo K. n Kim. A genetic algorithm for multiple objective sequencing problems in mixed model assembly lines[J]. Computers Ops Res, 1998, 25(7): 675-690.

DOI: 10.1016/s0305-0548(98)00026-4

[10] H. M. Song, Y. Q. Han, R. J. Li. Genetic algorithms-based load smoothness in assembly line[J]. Machinery Design & Manufacture, 2002, (4): 28-30.

[11] Yeo Keun Kim, Yong Ju Kim, Yeongho Kim. Genetic algorithms for assembly line balancing with various objectives[J]. Computers & Industrial Engineering, 1996, 30(3): 397-409.

DOI: 10.1016/0360-8352(96)00009-5

[12] Yeo Keun Kim, Chul Ju Hyun, Yeongho Kim. Sequencing in mixed model assembly lines: A genetic algorithm approach[J]. Comput. Ops. Res., 1996, 23(12): 1131-1145.

DOI: 10.1016/s0305-0548(96)00033-0