Real-world supply chain management problems are highly complicated such that their
optimization procedure is computationally expensive due to the extensive dimensions and …

Inventory control has been a major point of discussion in industrial engineering and
operations research for over 100 years. Various advanced numerical methods can be used …

This article presents the development of a systematic method to generate and deploy data
analytics models in an Asset Administration Shell (AAS). This method aims to exchange and …

The open platform communications unified architecture (OPC UA) has received attention as
a standard for data interoperability in industries. In particular, OPC UA extends its …

Big data analytics (BDA) applications use machine learning algorithms to extract valuable
insights from large, fast, and heterogeneous data sources. New software engineering …

Although artificial intelligence and machine learning are currently extremely fashionable,
applying machine learning on real-life problems remains very challenging. Data scientists …

Inventory control problems arise in various industries, and each single real-world inventory
is replete with non-standard factors and subtleties. Practical stochastic inventory control …

Big data analytics (BDA) applications use machine learning to extract valuable insights from
large, fast, and heterogeneous data sources. The architectural design and evaluation of …

The use of machine learning (ML) has been widely used to control part dimensions during
production. Parts manufactured in the automotive sectors also use ML to obtain better …

Traditional design optimization is an iterative process of design, simulation, and redesign,
which requires extensive calculations and analysis. The designer needs to adjust and …