Suomeksi

Jussi Hakanen

Senior researcher, PhD
Adjunct Professor (industrial optimization)
Department of Mathematical Information Technology
University of Jyväskylä
Finland

jussi.hakanen(at)jyu.fi
Mobile +358 40 805 3260, fax: +358 14 260 2771
Office: AgC 426.3, Agora building

I am a member of the Industrial Optimization Group of the University of Jyväskylä, headed by Prof. Kaisa Miettinen.

I was a principal investigator in the FINNOPT project (1.8.2014-31.10.2015) funded by Tekes, the Finnish Funding Agency for Innovation
I am also a researcher in the DeCoMo project (funded by Tekes) where a Finnish Distinguished Professor Yaochu Jin from the University of Surrey, UK, is visiting the Industrial Optimization Group

Lectured courses:

TIEA382 Linear and Discrete optimization (spring 2012)
TIES483 Nonlinear Programming (spring 2014)
TIES592 Multiobjective Optimization and Industrial Applications (autumn 2010)
TIES598 Nonlinear Multiobjective Optimization (spring 2015)
TIES501 Master's Thesis Seminar (autumn 2013)

Research interests

Multiobjective optimization: industrial applications, methods, especially interactive methods, theory of multiobjective optimization

My main research interest is multiobjective optimization. Especially, I am interested in industrial applications of multiobjective optimization and that was also the topic of my doctoral thesis. Industrial problems are usually computationally challenging and there are several conflicting performance criteria that need to be considered simultaneously. Therefore, it is very important to have efficient optimization methods in industrial process design. When considering multiple conflicting criteria there is no unique optimal solution of the optimization problem, but instead a set of mathematically equal compromise solutions, that are often called Pareto optimal solutions. Selection of the final solution among equally good compromise solutions requires some additional information about the problem in question. The specialist who is able to evaluate and compare these mathematically equivalent solutions is called a decision maker. Interactive multiobjective optimization methods are computationally efficient (in the sense of compromise solutions computed during the solution process) and their solution procedure utilizes the preferences of the decision maker continuously during the interactive solution procedure which makes them well suited for industrial applications.

Single objective optimization: sensitivity analysis, efficient optimizers for complex problems

An important research area related to multiobjective optimization is single objective optimization. Many multiobjective optimization methods utilize single objective optimization within the multiobjective optimization algorithm. Thus, efficient single objective optimizers for computationally demanding problems can greatly improve the overall performance of the multiobjective optimization methods.

My list of publications (pdf)