Machine learning an artificial intelligence approach volume II
Machine learning an artificial intelligence approach volume II
A fast taboo search algorithm for the job shop problem
Management Science
Using data mining to find patterns in genetic algorithm solutions to a job shop schedule
Computers and Industrial Engineering
Machine Learning
Rescheduling Manufacturing Systems: A Framework of Strategies, Policies, and Methods
Journal of Scheduling
A review of machine learning in dynamic scheduling of flexible manufacturing systems
Artificial Intelligence for Engineering Design, Analysis and Manufacturing
Dynamic scheduling I: simulation-based scheduling for dynamic discrete manufacturing
Proceedings of the 35th conference on Winter simulation: driving innovation
Discovering Dispatching Rules Using Data Mining
Journal of Scheduling
A very fast TS/SA algorithm for the job shop scheduling problem
Computers and Operations Research
A comparison of machine-learning algorithms for dynamic scheduling of flexible manufacturing systems
Engineering Applications of Artificial Intelligence
Training a neural network to select dispatching rules in real time
Computers and Industrial Engineering
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A data mining based approach to discover previously unknown priority dispatching rules for job shop scheduling problem is presented. This approach is based on seeking the knowledge that is assumed to be embedded in the efficient solutions provided by the optimization module built using tabu search. The objective is to discover the scheduling concepts using data mining and hence to obtain a set of rules capable of approximating the efficient solutions for a job shop scheduling problem (JSSP). A data mining based scheduling framework is presented and implemented for a job shop problem with maximum lateness as the scheduling objective.