Browsing by Author "Metan, G."

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Open Access
Real time selection of scheduling rules and knowledge extraction via dynamically controlled data mining
(Taylor & Francis, 2010) Metan, G.; Sabuncuoglu, I.; Pierreval, H.
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.
Open Access
A simulation based learning meachanism for scheduling systems with continuous control and update structure
(IEEE, 2005) Metan, G.; Sabuncuoğlu, İhsan
A simulation based learning mechanism is proposed in this study. The system learns in the manufacturing environment by constructing a learning tree and selects a dispatching rule from the tree for each scheduling period. The system utilizes the process control charts to monitor the performance of the learning tree which is automatically updated whenever necessary. Therefore, the system adapts itself for the changes in the manufacturing environment and works well over time. Extensive simulation experiments are conducted for the system parameters such as monitoring (MPL) and scheduling period lengths (SPL) on a job shop problem with objective of minimizing average tardiness. Simulation results show that the performance of the proposed system is considerably better than the simulation-based single-pass and multi-pass scheduling algorithms available in the literature.