C4.5: programs for machine learning
C4.5: programs for machine learning
Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Integration of simulation modeling and inductive learning in an adaptive decision support system
Decision Support Systems - Special issue on model management systems
Mining features for sequence classification
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Using conjunction of attribute values for classification
Proceedings of the eleventh international conference on Information and knowledge management
Knowledge-Based Approaches for Scheduling Problems: A Survey
IEEE Transactions on Knowledge and Data Engineering
A review of machine learning in dynamic scheduling of flexible manufacturing systems
Artificial Intelligence for Engineering Design, Analysis and Manufacturing
Instruction scheduling using evolutionary programming
ACC'08 Proceedings of the WSEAS International Conference on Applied Computing Conference
Learning heuristics for basic block instruction scheduling
Journal of Heuristics
Approximation of Dispatching Rules in Manufacturing Control Using Artificial Neural Networks
PADS '11 Proceedings of the 2011 IEEE Workshop on Principles of Advanced and Distributed Simulation
Supervised learning linear priority dispatch rules for job-shop scheduling
LION'05 Proceedings of the 5th international conference on Learning and Intelligent Optimization
Engineering Applications of Artificial Intelligence
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This paper introduces a novel methodology for generating scheduling rules using a data-driven approach. We show how to use data mining to discover previously unknown dispatching rules by applying the learning algorithms directly to production data. This approach involves preprocessing of historic scheduling data into an appropriate data file, discovery of key scheduling concepts, and representation of the data mining results in a way that enables its use for job scheduling. We also consider how by using this new approach unexpected knowledge and insights can be obtained, in a manner that would not be possible if an explicit model of the system or the basic scheduling rules had to be obtained beforehand. All of our results are illustrated via numerical examples and experiments on simulated data.