A Method for Setting the Optimal Production Status Based on Genetic Algorithm in a Discrete Assembly System

Abstract:

In a discrete assembly system, setting its optimal production status is one of the key works in assembly line balancing. Based on analyzing the objectives of assembly line control, a general flow for setting the optimal production status is proposed, and a method to identify rapidly the setting objects of production status is introduced. Then an optimal configuration solution for production status and its solving method in a station of an assembly line are established based on the genetic algorithm. At last, a wing assembly line is set as an example to validate this method, and the result shows that this method can provide a solution to optimize production status parameters for each station in this assembly line, which can reduce the resource idle time and cost, and so its resource utilization rate is improved.

• Periodical: Advanced Materials Research (Volume 421)
• Pages: 717-723
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.421.717
• Online since: December 2011
• Authors: Liang Dong, Zhen Guo Yan, Jie Zhang, Kun Peng Du, Yan Ping Wang
• Keywords: Aircraft Assembly Line Balancing, Genetic Algorithm (GA), Optimal Production Status

References

[1] Easton. A dynamic program with fathoming and dynamic upper bounds for the assembly line balance problem. Computers and Operation Research, 1990(17), pp.163-175.

DOI: 10.1016/0305-0548(90)90040-e

[2] John K. Kottas, Hon-Shiang Lan. A Stochastic Line Balancing Procedure. International Journal of Production Research, 1981, 19(2), pp.177-193.

[3] Suresh, Sahu. Stochastic Assembly Line Balancing Using Simulated Annealing. International Journal of Production Research, 1995(33), pp.975-991.

[4] X. Guo Z., Wong K., S. Y. S. Leung, J. T. Fan S. F. Chan. A genetic-algorithm-based optimization model for scheduling flexible assembly lines. Intentional J Journal Advance Manufacturing Technology, 2008, 36, p.156–168.

DOI: 10.1007/s00170-006-0818-6

[5] Jing HU, Zeng-qiang JIANG, Maogen GE, Weisha DU. Study on balance problem of mixed-model assembly line based on improved genetic algorithm. JOURNAL OF HEFEI UNIVERSITY OF TECHNOLOGY, 2010, 33(7), pp.1006-1010.(In Chinese)

[6] Qian Wang, Liang Shi. Application of Genetic Algorithm in Optimizing Mixed-model Assembly Line Balancing. Microcomputer Applications, 2010, 26(12), pp.37-39. (In Chinese)

[7] Jie Zhang. Research on Task Balance and Field Control Techniques for Distributed Discrete Assembly System. Northwestern Polytechnical University, 2011 Postdorctoral Research, P 58-68. (In Chinese)