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An unpredictable breakdown often occurs and tends to complicate production scheduling in a steelmaking-continuous casting (SCC) plant. Because of particular characteristics and technology constraints of the SCC plant, traditional robust scheduling often provides an excessively conservative solution. This paper proposes an effective proactive scheduling that utilizes robustness adopting a distribution curve of a system performance created as a mix-integer model. The proposed robustness is designed to work effectively with the existing factory operation and is based on uncertainty assessment. In this paper, artificial neural network (ANN) is adopted with a challenge of designing an accurate model due to the model complexity from the discrete and nonlinear properties of the system performance. The ANN model is achieved by applying k-mean clustering, which decomposes machines to smaller groups having similar effect to the uncertainty. A case study based on data from a real SCC plant is conducted to demonstrate the methodology. The experimental result shows that the proposed methodology is successful in designing a robust schedule that provides a lower production cost under an acceptable breakdown probability while also consuming less computational time compared with the traditional approach.

A study on ultrasound kidney images using proposed dominant Gabor wavelet is made for the automated diagnosis and classification of few important kidney categories namely normal, medical renal diseases and cortical cyst. The acquired images are initially preprocessed to retain the pixels of kidney region. Out of 30 Gabor wavelets, a unique dominant Gabor wavelet is determined by estimating the similarity metrics between original and reconstructed Gabor image. The Gabor features are then evaluated for each image. These derived features are mapped onto 2D feature space using k-mean clustering algorithm to group the data of similar class. The decision boundaries are formulated using linear discriminant function between the data sets of three kidney categories. A k-NN classifier module is used to identify the query input US kidney image category. The results show that the proposed dominant Gabor wavelet provides the classification efficiency of 87.33% for NR, 76.66% for MRD and 83.33% for CC. The overall classification efficiency improves by 18.89% compared to the classifier trained with features obtained by considering all the Gabor wavelets. The outputs of the proposed decision support systems are validated with medical expert to measure the actual efficiency. Also the overall discriminating ability of the systems is accessed with performance evaluation measure – f-score. It has been observed that the dominant Gabor wavelet improves the classification efficiency appreciably. Hence, the proposed method enhances the objective classification and explores the possibility of implementing a computer-aided diagnosis system exclusively for ultrasound kidney images.