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The classical multi-level capacitated lot-sizing problem is often not suitable to correctly capture resource requirements and precedence relations. We tackle this issue by explicitly modeling these two aspects. Two models are presented; one considering batch production and the other one allowing lot-streaming. Comparisons with traditional models demonstrate the capability of the new approach to...

Processes of a real world manufacturer involve a number of batch-processing operations. Generally, these operations require the decision on batch sizes of products and there is no specific rule to set batch sizes of all products simultaneously so that the sum of the weighted expected cycle times of all products is minimized. The weights represent different levels of importance for products. In ...

With the rise of the web 2.0 and the Internet of things, it has become feasible to track all kinds of information over time, in particular fine-grained user activities and sensor data on their environment and even their biometrics. However, while efficiency remains mandatory for any application trying to cope with huge amounts of data, only part of the potential of today’s Big Data repositories...

Robust PCA methods are typically based on batch optimization and have to load all the samples into memory during optimization. This prevents them from efficiently processing big data. In this paper, we develop an Online Robust PCA (OR-PCA) that processes one sample per time instance and hence its memory cost is independent of the number of samples, significantly enhancing the computation and st...

Ensemble learning methods train combinations of base models, which may be decision trees, neural networks, or others traditionally used in supervised learning. Ensemble methods have gained popularity because many researchers have demonstrated their superior prediction performance relative to single models on a variety of problems especially when the correlations of the errors made by the base m...

Abstract Implementing complex algorithms for big data, artificial intelligence, and graph processing requires enormous effort. Succinct, declarative programs to solve problems that can be efficiently executed batching streaming data are in demand. This paper presents Nexus, a distributed Datalog evaluation system. It evaluates using the semi-naive algorithm batch incremental asynchronous iterat...