Real time selection of scheduling rules and knowledge extraction via dynamically controlled data mining

Date
2010
Authors
Metan, G.
Sabuncuoglu, I.
Pierreval, H.
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Instructor
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
Journal Title
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Volume Title
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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.

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Keywords
Adaptive control, Data mining, Simulation, Dispatching rules, Dynamic scheduling, Game theory, Inventory management, Pricing theory, Radio frequency identification, Scheduling
Citation
Published Version (Please cite this version)