International Journal of Man-Machine Studies - Special Issue: Knowledge Acquisition for Knowledge-based Systems. Part 5
The nature of statistical learning theory
The nature of statistical learning theory
Automatic simulation model generation for simulation-based, real-time shop floor control
Computers in Industry
Feature Selection for Knowledge Discovery and Data Mining
Feature Selection for Knowledge Discovery and Data Mining
Machine Learning
Data Mining: Concepts and Techniques
Data Mining: Concepts and Techniques
Robotics and Computer-Integrated Manufacturing
GA-based learning bias selection mechanism for real-time scheduling systems
Expert Systems with Applications: An International Journal
A comparison of machine-learning algorithms for dynamic scheduling of flexible manufacturing systems
Engineering Applications of Artificial Intelligence
LEARNING-BASED SCHEDULING OF FLEXIBLE MANUFACTURING SYSTEMS USING SUPPORT VECTOR MACHINES
Applied Artificial Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Clustering of the self-organizing map
IEEE Transactions on Neural Networks
Mining association rules for the quality improvement of the production process
Expert Systems with Applications: An International Journal
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To confirm semiconductor wafer fabrication (FAB) operating characteristics, the scheduling decisions of shop floor control systems (SFCS) must develop a multiple scheduling rules (MSRs) approach in FABs. However, if a classical machine learning approach is used, an SFCS in FABs knowledge base (KB) can be developed by using the appropriate MSR strategy (this method is called an intelligent multi-controller in this study) as obtained from training examples. A classical machine learning approach main disadvantage is that the classes (scheduling decision variables) to which training examples are assigned must be pre-defined. This process becomes an intolerably time-consuming task. In addition, although the best decision rule can be determined for each scheduling decision variable, the combination of all the decision rules may not simultaneously satisfy the global objective function. To address these issues, this study proposes an intelligent multi-controller that incorporates three main mechanisms: (1) a simulation-based training example generation mechanism, (2) a data preprocessing mechanism, and (3) a self-organizing map (SOM)-based MSRs selection mechanism. These mechanisms can overcome the long training time problem of the classical machine learning approach in the training examples generation phase. Under various production performance criteria over a long period, the proposed intelligent multi-controller approach yields better system performance than fixed decision scheduling rules for each of the decision variables at the start of each production interval.