E3S Web Conf.
Volume 95, 2019The 3rd International Conference on Power, Energy and Mechanical Engineering (ICPEME 2019)
|Number of page(s)||4|
|Published online||13 May 2019|
An Improved Genetic Algorithms for Multi-objective Hybrid Flow-shop Scheduling Problem
Department of Mechanical Engineering, Beijing Institute of Technology, 5 South Zhongguancun Street, Haidian District, Beijing, China
2 The six branch factory, Beijing Xinghang Mechanical and Electrical Equipment Co., Ltd., No. 9, Dongwangzoobei Road, Fengtai District, Beijing, China
To deal with the multi-objective hybrid flow Shop Scheduling Problem (HFSP), an improved genetic algorithms based on parallel sequential moving and variable mutation rate is proposed. Compared with the traditional GA, the algorithm proposed in this paper uses the two-point mutation rule based on VMR to find the global optimum which can make the algorithm jump out of the local optimum as far as possible, once it falls into the local optimum quickly. Decoding rules based on parallel sequential movement ensures that the artifact can start processing in time, so that the buffer between stages in the flow-shop is as little as possible, and the production cycle is shortened. Finally, a program was developed with the actual data of a workshop to verify the feasibility and effectiveness of the algorithm. The result shows that the algorithm achieves satisfactory results in all indexes mentioned above.
© The Authors, published by EDP Sciences, 2019
This is an open access article distributed under the terms of the Creative Commons Attribution License 4.0 (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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