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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
      Source Title
      International Journal of Production Research
      Print ISSN
      0020-7543
      Electronic ISSN
      1366-588X
      Publisher
      Taylor & Francis
      Volume
      48
      Issue
      23
      Pages
      6909 - 6938
      Language
      English
      Type
      Article
      Item Usage Stats
      125
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      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.
      Keywords
      Adaptive control
      Data mining
      Simulation
      Dispatching rules
      Dynamic scheduling
      Game theory
      Inventory management
      Pricing theory
      Radio frequency identification
      Scheduling
      Permalink
      http://hdl.handle.net/11693/22089
      Published Version (Please cite this version)
      http://dx.doi.org/10.1080/00207540903307581
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      • Department of Industrial Engineering 684
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