Recent news

Profiling action of the university "Decision Analytics utilizing Causal Models and Multiobjective Optimization (DEMO)" has received funding from the Academy of Finland

Tinkle Chugh has won the best student paper award at the CEC2017 conference

Tinkle Chugh succesfully defended his PhD thesis "Handling expensive multiobjective optimization problems with evolutionary algorithms" on June 19. The opponent was Associate professor Michael Emmerich (Leiden University). Congratulations Tinkle!

ArtificiaI Intelligence Supported Decision Making in Industry - Second Workshop in FiDiPro Project on 5th of September 2017 Register now!

Karthik Sindhya has now a title of docent in "evolutionary multiobjective optimization and industrial applications"

FiDiPro professorship at The Industrial Optimization Group

Research Group in Industrial Optimization

Faculty of Information Technology

University of Jyväskylä, Finland


The Industrial Optimization Group of the University of Jyväskylä, Finland, is a part of the Faculty of Information Technology and is headed by Prof. Kaisa Miettinen (since 1998). The research interests of the group are focused on (nonlinear) multiobjective optimization in the presence of conflicting objectives, including e.g.

  • method development, with focus on
    • interactive methods
    • evolutionary and hybrid methods
  • theoretical aspects
  • software development
  • real-world applications
    • simulation-based problems
    • data-driven problems (prescriptive analytics)
Overall, the work is inspired by real-life applications. Interactive multiobjective optimization methods are at the very core of the interests of the group.

The Industrial Optimization Group is one of the few groups that specializes in implementing interactive methods, in particular, as open access software. A DESDEO framework is under preparation.

Besides simulation-based optimization, the group is interested in data-driven decision support and in particular prescriptive analytics whch we call decision analytics when multiple conflicting objectives are considered in making recommendations as decisions based on data available. We are also interested in employing artificial intelligence and machine learning and, in particular, explainable artificial intelligence.

In the name of the group, industrial optimization is indicating that in general theoretical and methodological development, the focus is typically on methods which are suitable and applicable in the case of industrial applications. Even though the methods applied are typically based on strong mathematical foundations, in practice, the applications may lack nice mathematical structures (they can be e.g. black box models and computationally expensive) and these practical characteristics must be taken into account when developing methods. Another characteristic of the methods developed is application-independence. In other words, behind the application-specific user-interface, the optimization method can be the same for designing paper machines or planning radiotherapy treatment.

Among others, the industrial applications considered deal with improvement of product properties, making production processes and their controls more efficient, or finding the best shape or structure etc.