This paper addresses a scheduling problem motivated by scheduling of diffusion operations in the wafer fabrication facility. In the target problem, jobs arrive at the batch machines at different time instants, and only jobs belonging to the same family can be processed together. Parallel batch machine scheduling typically consists of three types of decisions – batch forming, machine assignment,...

Data-intensive batch jobs increasingly compete for resources with customer-facing online workloads in modern data centers. Today, the two classes of workloads run on separate infrastructures using different resource managers that pursue different objectives. Batch processing systems strive for coarse-grained throughput whereas online systems must keep the latency of fine-grained enduser request...

Abstract Whereas the buffer content of batch-service queueing systems has been studied extensively, the customer delay has only occasionally been studied. The few papers concerning the customer delay share the common feature that only the moments are calculated explicitly. In addition, none of these surveys consider models including the combination of batch arrivals and a server operating under...

It has been experimentally observed that distributed implementations of mini-batch stochastic gradient descent (SGD) algorithms exhibit speedup saturation and decaying generalization ability beyond a particular batch-size. In this work, we present an analysis hinting that high similarity between concurrently processed gradients may be a cause of this performance degradation. We introduce the no...

We recently introduced a new stochastic Petri net model called “batch deterministic and stochastic Petri nets” (BDSPNs) capable of describing the synchronization of discrete and batch token flows in discrete batch processes. It is a powerful formal model for the study of inventory systems and supply chains where materials are processed or ordered in finite discrete quantities (batches) and many...

Effective regularisation during training can mean the difference between success and failure for deep neural networks. Recently, dither has been suggested as alternative to dropout for regularisation during batch-averaged stochastic gradient descent (SGD). In this article, we show that these methods fail without batch averaging and we introduce a new, parallel regularisation method that may be ...

Batch service is a kind of specific stochastic service system which has broad application background. However, in practical, batch service exists commonly as one or several steps in a production or business process. Workflow management system (WfMS) is a powerful tool to support business processes modeling and execution. It has gained extensive attention from both industry and academic domain. ...

The relationship between lipid content and tolerance to freezing at -50 degrees C was studied in Saccharomyces cerevisiae grown under batch or fed-batch mode and various aeration and temperature conditions. A higher free-sterol-to-phospholipid ratio as well as higher free sterol and phospholipid contents correlated with the superior cryoresistance in dough or in water of the fed-batch-grown com...

Generative moment matching network (GMMN), which is based on the maximum mean discrepancy (MMD) measure, is a generative model for unsupervised learning, where the mini-batch stochastic gradient descent is applied for the update of parameters. In this work, instead of obtaining a mini-batch randomly, each mini-batch in the iterations is selected in a submodular way such that the most informativ...

Batch Reinforcement Learning has established itself as a valuable alternative to develop learning and adaptive agents. Batch Reinforcement Learning algorithms are characterized by obtaining a policy from a set of collected data. Common methods apply adapted versions of RL update rules, such as QLearning, on the transitions of the batch, building a pattern set. The target values of the pattern r...