03/2019 Data Analytics and Optimization for Decision Support

Published : 19.10.2017 | Categories: Call For Papers

Special Issue:

Traditional methods for addressing an industrial optimization problem usually follows the workflow of “Modeling-Algorithm-Analysis”. This means first formulating a specific industrial problem and establishing its mathematical optimization model, then finding or designing an algorithm/method addressing the problem, and finally analyzing the results. This method is primarily used when not enough data is available.

However, with the pervasive applications of the new generation of information technologies (such as cloud computing, internet of things, big data, mobile internet, artificial intelligence) in industry, a massive amount of data is generated and collected in the entire process of industry, which makes “data-driven optimization” a new effective method for industrial optimization.

Therefore, we suggest this special issue which focusses especially on data analytics AND optimization for decision support. The direction mentioned above will change, and the key question is how might optimization techniques support modern data analytics?

On the other side, the type of big data generated in the entire process is still unclear. Furthermore, the transformation of the traditional mathematical optimization model to a data-based optimization model (i.e., network-based optimization mode), collection and management of useful data, and the extraction and utilization of useful information from such huge and dynamic “big data” are challenging tasks. This has recently motivated researchers and scientists to explore new methods and technologies for industrial applications of complex network and big data in industrial optimization, especially machine learning and artificial intelligence.


The objective of this Special Issue on “Data Analytics and Optimization for Decision Support is to present the latest advances and developments of methods, techniques, systems and tools dedicated to that relationship between data analytics and optimization.

Topics include, but are not limited to, the following:

  • Data Mining and Optimization with Big Data
  • Industrial Applications of Big Data
  • Complex Network Based Information System Modeling and Optimization
  • Modern Optimization Techniques in the Context of Analytics
  • Data-Driven Enterprise Information Systems Modeling
  • Internet-of-Things
  • Predictive and Prescriptive Maintenance
  • IoT – Cloud based Optimization
  • Simulation-based Optimization
  • Data Driven Modeling and Simulation


Anticipated publication date: June 2019

Paper submission due: 01.07.2018
Notification of authors: 26.08.2018
Revision due: 28.10.2018
Notification of authors: 16.12.2018
Completion of a second revision (if needed): 20.01.2019

Guest Editors

Prof. Wolfgang Bein
Department of Computer Science, University of Nevada, Las Vegas
CITA Center for Information Technology and Algorithms
email: wolfgang.bein@unlv.edu

Prof. Stefan Wolfgang Pickl
Department of Computer Science, UBw München
email: stefan.pickl@unibw.de

Prof. Fei Tao
Professor of and Vice Dean of School of Automation Science and
Electrical Engineering (SASEE) – Beihang University (BUAA),
Beijing 100191, P. R. China
Email: ftao@buaa.edu.cn