Data-driven Decision Support with Multiobjective Optimization (DAEMON) (DAEMON)
Main funder
Funder's project number: 322221
Funds granted by main funder (€)
- 592 530,00
Funding program
Project timetable
Project start date: 01/09/2019
Project end date: 31/08/2023
Summary
We develop modular building blocks for modelling and solving data-driven decision making problems utilizing tools of machine learning and multiobjective optimization. As inspiration and for testing the development, we have data sets from three application areas. The implementation significantly extends the open source code DESDEO framework designed for simulation-based problems.
Principal Investigator
Primary responsible unit
Follow-up groups
Profiling area: Decision analytics utilizing causal models and multiobjective optimization (University of Jyväskylä JYU) DEMO; 2017-2021
Related publications and other outputs
1 of 4
- Let decision-makers direct the search for robust solutions : An interactive framework for multiobjective robust optimization under deep uncertainty (2025) Shavazipour, Babooshka; et al.; A1; OA
- A Performance Indicator for Interactive Evolutionary Multiobjective Optimization Methods (2024) Aghaei Pour, Pouya; et al.; A1; OA
- An experimental design for comparing interactive methods based on their desirable properties (2024) Afsar, Bekir; et al.; A1; OA
- DESMILS : a decision support approach for multi-item lot sizing using interactive multiobjective optimization (2024) Kania, Adhe; et al.; A1; OA
- Group Decision Making in Multiobjective Optimization : A Systematic Literature Review (2024) Pajasmaa, Juuso; et al.; A2; OA
- Machine learning models for assessing risk factors affecting health care costs : 12-month exercise-based cardiac rehabilitation (2024) Hautala, Arto J.; et al.; A1; OA
- SCORE Band Visualizations : Supporting Decision Makers in Comparing High-Dimensional Outcome Vectors in Multiobjective Optimization (2024) Saini, Bhupinder S.; et al.; A1; OA
- Comparing interactive evolutionary multiobjective optimization methods with an artificial decision maker (2023) Afsar, Bekir; et al.; A1; OA
- Component-based thinking in designing interactive multiobjective evolutionary methods (2023) Lárraga, Giomara; et al.; A4; OA; 979-8-4007-0120-7
- Designing empirical experiments to compare interactive multiobjective optimization methods (2023) Afsar, Bekir; et al.; A1; OA
