The 34th Jyvaskyla Summer School will be organized on August 4–15, 2025 in the beautiful campus located next to the lake, at the University of Jyväskylä, Finland.

The Summer School welcomes students from all around the world to learn from top-level scientists, expand their professional network, and create new memories. The Summer School offers courses for advanced Master’s students, PhD students and post-docs in various fields of natural sciences, mathematics and information technology including multiobjective optimization. All courses are taught in English by distinguished researchers. Participation in all Summer School courses is free of charge (but the participants are responsible for their own travel and accommodation costs). For more information about the Summer School, please visit https://www.jyu.fi/jss

Deadline for applications to attend courses: end of April. For more information, see How to apply to Jyväskylä Summer School | University of Jyväskylä (jyu.fi)

Information about the courses available: https://www.jyu.fi/en/study-with-us/summer-and-winter-schools/jyvaskyla-summer-school/jyvaskyla-summer-school-course-programme

For example, we offer the following course related to multiobjective optimization on August 11-15:

COM3: Interactive Multiobjective Optimization: Applications and Tools to Support Decision Making   

Lecturers: Dr. Giovanni Misitano, Dr. Bhupinder Saini, Dr. Giomara Lárraga, Dr. Juho Roponen and Juuso Pajasmaa (all from the Multiobjective Optimization Group at the University of Jyvaskyla) 

Modes of study: Attendance and exercises, optional final project

Credits: 2/4 ECTS 

The course will award 2-4 credits depending which option participants choose:

– Option 1: 2 credits for attending lectures and exercise sessions;

– Option 2: 2 additional credits will be awarded to participants who, on top of attending lectures and practical sessions, also complete a final project.

Evaluation: Pass/Fail. The minimum requirement for passing the course is to take part in the daily lectures and exercise sessions.

Contents: Real-life optimization problems rarely involve only a single objective. Instead, meaningful decision-making requires optimizing multiple conflicting objectives simultaneously. To navigate these conflicts and find a satisfactory solution, preference information from a domain expert, the decision maker, is essential. Based on these preferences, we can search for solutions that best align with their goals. However, exploring such problems is often computationally and cognitively demanding, making decision support crucial.

Understanding how a decision maker expresses their preferences and how these preferences are utilized is crucial. This course introduces interactive multiobjective optimization methods, which address these key questions. Given the increasing role of data in real-world decision-making, we will also explore techniques for modeling data-driven multiobjective optimization problems. Through practical examples, we will examine and solve various real-world multiobjective problems, including both data-driven and simulator-based cases.

We will explore various interactive multiobjective optimization methods, including scalarization-based approaches and evolutionary algorithms. Additionally, we will examine how different methods can be combined and guide participants in developing their own approaches. The course will also cover graphical interfaces for interactive methods.. Hands-on experience with these concepts will be facilitated through the 2.0 version of the DESDEO framework [1], which provides the necessary tools for practical application.

Each day will focus on a central theme in interactive multiobjective optimization. The morning sessions will introduce key concepts related to the day's theme, while the afternoon sessions will provide hands-on experience applying these ideas using the DESDEO framework. To reinforce learning, an optional final project will be available, allowing participants to deepen their understanding and even contribute to the open-source DESDEO framework.

The final project is required for those seeking 4 ECTS credits. Participants who attend daily lectures and exercise sessions without the final project will receive 2 ECTS credits.

[1] G. Misitano, B. S. Saini, B. Afsar, B. Shavazipour and K. Miettinen, 'DESDEO: The Modular and Open Source Framework for Interactive Multiobjective Optimization,' IEEE Access, vol. 9, pp. 148277-148295, 2021, doi: 10.1109/ACCESS.2021.3123825.

Learning outcomes: After completing the course, participants will have a solid understanding of multiobjective optimization and the interactive methods that support decision-making. They will be familiar with both scalarization-based and evolutionary interactive methods, as well as various visualization and GUI techniques for assisting decision makers. Additionally, students will gain the necessary prerequisites to apply the DESDEO framework in modeling and solving their own data-driven optimization problems.

Participants are encouraged to bring their own laptops and optimization problems (if any) to the course!

Prerequisites: Participants are expected to have prior knowledge of the following concepts:

– Python (basic programming skills)

– Fundamentals of calculus

– Optimization and mathematical programming

– Basics of single-objective optimization

Please, forward information about the summer school to colleagues and students who could be interested in attending.

With best regards, Kaisa Miettinen

************************************

Vice-rector Kaisa Miettinen, PhD

FI-40014 University of Jyvaskyla, Finland

Professor in Industrial Optimization

Multiobjective Optimization Group: https://optgroup.it.jyu.fi/

– – –

* Director of the thematic research area Decision Analytics utilizing Causal Models

and Multiobjective Optimization, http://www.jyu.fi/demo

– – –

tel. +358 50 3732247 (mob.)

email: kaisa.miettinen at jyu.fi

homepage: http://www.mit.jyu.fi/miettine and http://www.mit.jyu.fi/miettine/engl.html

* Developing open source software framework DESDEO for interactive methods: https://desdeo.it.jyu.fi

My book: Nonlinear Multiobjective Optimization, Kluwer (Springer):  http://www.mit.jyu.fi/miettine/book/

* My publications: http://www.mit.jyu.fi/miettine/publ.html