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Title of Journal: Int J Adv Manuf Technol

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Abbravation: The International Journal of Advanced Manufacturing Technology

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Springer-Verlag

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DOI

10.1002/chin.199910133

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1433-3015

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A jobclassifying and datamining approach for est

Authors: Toly Chen
Publish Date: 2011/12/03
Volume: 62, Issue: 1-4, Pages: 317-328
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Abstract

Before making a due date commitment to our customer we need to estimate the cycle times of jobs For this reason many of the mostadvanced methods classify jobs before or after estimating the cycle times However job classification is not directly helpful to optimize the performances of these estimation methods To solve this problem a jobclassifying and datamining approach is proposed in this study to improve the performance of cycle time estimation by optimizing the results of job classification In addition some association rules are also extracted from the estimation results to facilitate the practical application of the proposed methodology According to the results of a case study the jobclassifying and datamining approach achieved a better estimation performance and could produce some useful estimation rules

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Other Papers In This Journal:
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  12. Selection of optimal cutting conditions for pocket milling using genetic algorithm
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  14. Applying bidirectional kinematics to assembly error analysis for five-axis machine tools with general orthogonal configuration
  15. Simulation modeling for comparative evaluation of supply chain management strategies
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