Data Mining for ERW Welded Tube Scheduling Rules Based on Decision Tree

Ning Zhao; Shao Hua Dong; Qing Tian

doi:10.4028/www.scientific.net/AMR.403-408.1804

Paper Titles

The Fault Detection Strategies Used in Mobile Communication Network
p.1786

Improved Algorithm Based on TFIDF in Text Classification
p.1791

Optical Surface Position Encode and Application Research
p.1795

The New Management Model of Real Estate Enterprise Base on Relationship Marketing
p.1799

Data Mining for ERW Welded Tube Scheduling Rules Based on Decision Tree
p.1804

The Application of AJAX Technology on WebGIS
p.1808

Effect of Phase Shift Deviation on the Performance of an Optical 90^o Hybrid without Cross-over
p.1813

Research of Wavelet Packet Analysis in the De-Noising of Ultrasonic Echo Signal
p.1817

Research on De-Noising of Ultrasonic Detection Signal via Multiwavelets with Different Preprocessing Methods
p.1823

HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 403-408Data Mining for ERW Welded Tube Scheduling Rules...

Data Mining for ERW Welded Tube Scheduling Rules Based on Decision Tree

Abstract:

In order to optimize electric- arc welding （ERW） welded tube scheduling , the paper introduces data cleaning, data extraction and transformation in detail and defines the datasets of sample attribute, which is based on analysis of production process of ERW welded tube. Furthermore, Decision-Tree method is adopted to achieve data mining and summarize scheduling rules which are validated by an example.

You might also be interested in these eBooks

View Preview

Info:

Periodical:

Advanced Materials Research (Volumes 403-408)

Pages:

1804-1807

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.403-408.1804

Citation:

Cite this paper

Online since:

November 2011

Authors:

Ning Zhao, Shao Hua Dong, Qing Tian

Keywords:

Data Mining (DM), Decision Tree, Scheduling Rules, Welded Tube

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

Citation:

References

[1] Elamghaby S.E. The Economic Lot Scheduling Problem [J]. Review and Extensions, 1978, 24:587-598.

Google Scholar

[2] Lee S D, Rung J.M. Production lot sizing in failure prone two-systems [J]. European Journal of Operational Research, 2000,123:42-60.

DOI: 10.1016/s0377-2217(99)00089-2

Google Scholar

[3] MoursliO, Pochet, Y. A branch-and-bound algorithm for the hybrid flowshop [J]. International Journal of Production Economics, 2000, 64:113~125.

DOI: 10.1016/s0925-5273(99)00051-1

Google Scholar

[4] Chern C, Hsieh C H. A heuristic master planning algorithm toschedule orders under due date requirements and capacitylimits. Proceedings of the 33rdannual meeting of DecisionScience Institute [J]. San Diego California, 2002:1842-1847.

Google Scholar

[5] J. R. Quinlan. Induction of decision trees [J]. Machine Learning, 1986,1(1):12 .

Google Scholar

[6] Potts C N, Wassenhove L N V. A decomposition algorithm for the single machine total tardiness problem [J]. Operations Research Letters, 1982,1(5):177-181.

DOI: 10.1016/0167-6377(82)90035-9

Google Scholar

[7] Michael J.Shaw. Chandrasekar Subramaniam. Knowledge management and data mining for marketing [J]. Decision Support System , 2001,31：127~137

DOI: 10.1016/s0167-9236(00)00123-8

Google Scholar

[8] Fayyad. Information Visualization in Data Mining and Knowledge Discovery[J]. Morgan Kaufman Publishers, 2002,09:76.

Google Scholar