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      • Department of Industrial Engineering
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      Real time selection of scheduling rules and knowledge extraction via dynamically controlled data mining

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      Author
      Metan, G.
      Sabuncuoglu, I.
      Pierreval, H.
      Date
      2010
      Journal Title
      International Journal of Production Research
      ISSN
      0020-7543 (print)
       
      1366-588X (online)
       
      Publisher
      Taylor & Francis
      Volume
      48
      Issue
      23
      Pages
      6909 - 6938
      Language
      English
      Type
      Article
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      Please cite this item using this persistent URL
      http://hdl.handle.net/11693/22089
      Abstract
      A new scheduling system for selecting dispatching rules in real time is developed by combining the techniques of simulation, data mining, and statistical process control charts. The proposed scheduling system extracts knowledge from data coming from the manufacturing environment by constructing a decision tree, and selects a dispatching rule from the tree for each scheduling period. In addition, the system utilises the process control charts to monitor the performance of the decision tree and dynamically updates this decision tree whenever the manufacturing conditions change. This gives the proposed system the ability to adapt itself to changes in the manufacturing environment and improve the quality of its decisions. We implement the proposed system on a job shop problem, with the objective of minimising average tardiness, to evaluate its performance. Simulation results indicate that the performance of the proposed system is considerably better than other simulation-based single-pass and multi-pass scheduling algorithms available in the literature. We also illustrate knowledge extraction by presenting a sample decision tree from our experiments.
      Published as
      http://dx.doi.org/10.1080/00207540903307581
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