A Method on Process Planning Decision Based on Genetic Algorithm

Dan Dan Tan; Yi Ming  Zhang

doi:10.4028/www.scientific.net/AMR.760-762.1902

Paper Titles

On Real-Time Modeling of 3D Terrain Database
p.1884

Application of Similarity Algorithm in the Assessment of the Students Curriculum Design
p.1888

Coordination Algorithm Design of the Distributed Storage Proxy Servers for Streaming Media
p.1892

Analysis of Web Log Data Mining Based on Improved Fuzzy Clustering Algorithm
p.1896

A Method on Process Planning Decision Based on Genetic Algorithm
p.1902

A Hybird Virtual Machine Placement Aglrithm for Virtualized Desktop Infrastructure
p.1906

Curve-Skeleton Extraction Using Appropriate Threshold Optimization
p.1911

A General Vertex Partition Refinement Algorithm for Graph Isomorphism
p.1919

Research of Clustering Algorithm Based on Different Data Field Model
p.1925

HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 760-762A Method on Process Planning Decision Based on...

A Method on Process Planning Decision Based on Genetic Algorithm

Abstract:

This paper proposes a method for optimizing the process planning with GA in ERP, analyzes the factors relating to production planning decisions in ERP. On this basis, builds the mathematical model of process planning and gives the step of solving mathematical model using GA through a specific example.

You might also be interested in these eBooks

View Preview

Info:

Periodical:

Advanced Materials Research (Volumes 760-762)

Pages:

1902-1905

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.760-762.1902

Citation:

Cite this paper

Online since:

September 2013

Authors:

Dan Dan Tan, Yi Ming Zhang

Keywords:

Enterprise Resource Planning (ERP), Genetic Algorithm (GA), Process Planning Decision

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

Citation:

References

[1] Figielska, E., A genetic algorithm and a simulated annealing algorithm combined with column generation technique for solving the problem of scheduling in the hybrid flowshop with additional resources. Chem Ind Eng, 56(1): 142–151. (2009).

DOI: 10.1016/j.cie.2008.04.008

Google Scholar

[2] Liu Ying, Zhou Hui, Yu Wen-Fang, Image vector quantization coding based on Genetic algorithm, Proceedings of the 2003 IEEE , International conference on Robotics, Intelligent systems and Signal processing, Changsha, China-Oct. (2003).

DOI: 10.1109/rissp.2003.1285683

Google Scholar

[3] Deb, K. Genetic algorithms for function optimization. In F. Herrera and J. L Verdegay (Eds. )Genetic Algorithms and Soft Computing. Heidelberg: Physica-Verlag. (2000).

Google Scholar

[4] Rafael C. Gonzalez, Richard E. Woods and Steven L. Eddins, Digital image processing using MATLAB , Pearson Prentice Hall (2004).

Google Scholar

[5] Goldberg, D. E. Genetic Algorithms in Search, Optimization and Machine Learning. Addison-Wesley Publishing Company, Reading, Massachusetts, (1989).

Google Scholar

[6] Mealane Mitchell, An introduction to Genetic algorithm, The MIT Press (1996).

Google Scholar

[7] K. F. Man, K.S. Tang and S. Kwong, Genetic algorithms: Concepts and Applications, IEEE Trans. On Industrial electronics, vol. 43, no. 5, pp.519-534, Oct. (2006).

DOI: 10.1109/41.538609

Google Scholar

[8] He, Y.H. and Hui, C.W., A rule-based genetic algorithm for the scheduling of single-stage multi-product batch plants with parallel units. Comput Chem Eng, 32(12): 3067–3083. (2008).

DOI: 10.1016/j.compchemeng.2008.04.008

Google Scholar