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

• Published in: International journal of production research Vol. 48; no. 23; pp. 6909 - 6938
• Main Authors: Metan, Gokhan, Sabuncuoglu, Ihsan, Pierreval, Henri
• Format: Journal Article
• Language: English
• Published: Abingdon Taylor & Francis Group 01.12.2010; Taylor & Francis; Taylor & Francis LLC
• Subjects: adaptive control; Applied sciences; Charts; Computer science; control theory; systems; Computer simulation; Control charts; Data Mining; Data processing. List processing. Character string processing; Decision theory. Utility theory; Decision trees; Dispatching rules; dynamic scheduling; Dynamical systems; Dynamics; Entscheidungsbaum; Exact sciences and technology; Extraction; game theory; Inventory control, production control. Distribution; inventory management; Job shops; Manufacturing; Memory organisation. Data processing; Operational research and scientific management; Operational research. Management science; pricing theory; Process controls; Produktionssteuerung; radio frequency identification; Real time; Scheduling; Scheduling algorithms; Scheduling, sequencing; Scheduling-Verfahren; Simulation; Software; Statistical process control; Statistische Methode; Studies; Tree(graph); simulation; Job shop; radio frequency identification; Data mining; dynamic scheduling; Decision tree; Adaptive control; Delay; pricing theory; Statistical data; Pricing; Inventory control; Dynamic programming; Environment quality; Statistical process control; Data analysis; Control chart; Process control; Routing; Scheduling; Radiofrequency; Real time; Knowledge extraction; Game theory; Selection rule; inventory management; dispatching rules
• ISSN: 0020-7543; 1366-588X
• DOI: 10.1080/00207540903307581

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.