Priority rules for job shops with weighted tardiness costs
Management Science
Evaluation of FMS parameters on overall system performance
Computers and Industrial Engineering
Neural networks: algorithms, applications, and programming techniques
Neural networks: algorithms, applications, and programming techniques
Tolerating noisy, irrelevant and novel attributes in instance-based learning algorithms
International Journal of Man-Machine Studies - Special issue: symbolic problem solving in noisy and novel task environments
A study on decision rules of a scheduling model in an FMS
Computers in Industry
New advances for wedding optimization and simulation
Proceedings of the 31st conference on Winter simulation: Simulation---a bridge to the future - Volume 1
Genetic Algorithms Plus Data Structures Equals Evolution Programs
Genetic Algorithms Plus Data Structures Equals Evolution Programs
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Computers and Intractability; A Guide to the Theory of NP-Completeness
Computers and Intractability; A Guide to the Theory of NP-Completeness
Machine Learning
A review of machine learning in dynamic scheduling of flexible manufacturing systems
Artificial Intelligence for Engineering Design, Analysis and Manufacturing
A Recursive Partitioning Decision Rule for Nonparametric Classification
IEEE Transactions on Computers
ICCS '07 Proceedings of the 7th international conference on Computational Science, Part IV: ICCS 2007
Self-developing fuzzy expert system: a novel learning approach, fitting for manufacturing domain
Journal of Intelligent Manufacturing
Computers and Industrial Engineering
Engineering Applications of Artificial Intelligence
A holonic approach to flexible flow shop scheduling under stochastic processing times
Computers and Operations Research
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Dispatching rules are frequently used to schedule jobs in flexible manufacturing systems (FMSs) dynamically. A drawback, however, to using dispatching rules is that their performance is dependent on the state of the system, but no single rule exists that is superior to all the others for all the possible states the system might be in. This drawback would be eliminated if the best rule for each particular situation could be used. To do this, this paper presents a scheduling approach that employs machine learning. Using this latter technique, and by analysing the earlier performance of the system, 'scheduling knowledge' is obtained whereby the right dispatching rule at each particular moment can be determined. Three different types of machine-learning algorithms will be used and compared in the paper to obtain 'scheduling knowledge': inductive learning, backpropagation neural networks, and case-based reasoning (CBR). A module that generates new control attributes allowing better identification of the manufacturing system's state at any particular moment in time is also designed in order to improve the 'scheduling knowledge' that is obtained. Simulation results indicate that the proposed approach produces significant performance improvements over existing dispatching rules.