We consider an online scheduling problem in a parallel batch processing system with jobs in a batch being allowed to restart. Online means that jobs arrive over time, and all jobs’ characteristics are unknown before their arrival times. A parallel batch processing machine can handle up to several jobs simultaneously. All jobs in a batch start and complete at the same time. The processing time o...

The paper focuses on the development of a classification strategy to identify critic situation in batch process control. Data acquired from a batch execution is reduced by means of multiway principal component analysis in order to be assessed according to the statistical model of the process. Multiple situations have been categorized by a classification algorithm applied to the principal compon...

<span lang="EN-US">Clustering methods in data mining aim to group a set of patterns based on their similarity. In survey, heterogeneous information is established with various types scales like nominal, ordinal, binary, and Likert scales. A lack treatment leads loss scanty decision-making. Although many similarity measures have been established, solutions for clustering are still lacking....

Statistical procedures have been proposed to select the simulated system with the largest or smallest long-run average performance from among a finite number of alternative systems. In this paper, we focus on the indifference-zone formulation of the selection problem, meaning that we desire procedures that are guaranteed to select the best system with high probability when the long-run average ...

On-line monitoring of batch processes using multi-way independent component analysis has attracted much attention in both academia and industry. This paper focuses on two knotty issues concerning selecting dominant independent components without a standard criterion and determining the control limits of monitoring statistics in the presence of non-Gaussian distribution. To optimize the number o...

We provide a clustering algorithm that approximately optimizes the k-means objective, in the one-pass streaming setting. We make no assumptions about the data, and our algorithm is very light-weight in terms of memory, and computation. This setting is applicable to unsupervised learning on massive data sets, or resource-constrained devices. The two main ingredients of our theoretical work are: ...

Run-to-Run (R2R) control has been widely used in semiconductor manufacturing to compensate for process disturbance and to improve quality. The traditional R2R controller only takes the process output in a previous run as its input and generates an optimal recipe for the next run. In a multistage semiconductor manufacturing process, variations in upstream stations are propagated to downstream st...

This paper deals with the modelling of batch systems by means of high-level Petri nets. Batch systems are systems that transform a continuous raw material through continuous equipment (as heat exchanger for example) and batch equipment (for instance, batch reactor). These systems are frequently used by chemical of food industry. Batch systems deal with concurrent activities and resource allocat...

Mini-batch stochastic gradient descent and variants thereof have become standard for large-scale empirical risk minimization like the training of neural networks. These methods are usually used with a constant batch size chosen by simple empirical inspection. The batch size significantly influences the behavior of the stochastic optimization algorithm, though, since it determines the variance o...