A Self-Learning Algorithm Based on Support Vector Machine for Scheduling a Job-Shop-Like Knowledgeable Manufacturing Cell

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The job-shop-like knowledgeable manufacturing cell scheduling is a NP-complete problem and there has not been a completely valid algorithm for it until now. An algorithm with self -learning ability is proposed through the addition of precedence constraint of operations on the basis of directed graph. A method based on support vector machine is constructed to choose accurately interchangeable operations by small samples earning to obtain the better scheduling. The classification accuracy can be improved by the continuous addition of new instances to the sample library. The results of simulation show that the algorithm performs well for the job-shop-like knowledgeable manufacturing cell.