Dagstuhl-Seminar 26041
Uncertainty Quantification in Multiobjective Optimization
( 18. Jan – 23. Jan, 2026 )
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Organisatoren
- Richard Allmendinger (University of Manchester, GB)
- Carlos M. Fonseca (University of Coimbra, PT)
- Susan R. Hunter (Purdue University, US)
- Serpil Sayin (Koç University - Istanbul, TR)
Kontakt
- Michael Gerke (für wissenschaftliche Fragen)
- Christina Schwarz (für administrative Fragen)
Multiobjective optimization (MO), a discipline within systems science that provides models, theories, and methodologies to address decision-making problems under conflicting objectives, has several applications in a number of areas, such as agriculture and land management, engineering design, emergency services and disaster response, healthcare and logistics among others. This Dagstuhl Seminar is motivated by the fact that many real-world problems are subject to uncertainty, including about the system itself and the preferences of the decision-maker. There has been limited research on quantifying the uncertainty arising from each of these sources, and little is known about how those uncertainties interact. Research focusing on quantifying uncertainties from different sources and the way they jointly impact final decisions is much needed.
The seminar seeks to identify the most pressing needs for quantifying uncertainty in MO problems that arise in industry, along with the most promising theoretical and methodological developments that will lead to efficient algorithms for addressing the identified needs. Toward this end, the seminar aims to bring together academic and industry experts as well as early-career researchers from different research disciplines, including Evolutionary Multiobjective Optimization, Multiple Criteria Decision Making, and Stochastic/Simulation Optimization, to discuss and propose novel ideas related to modeling, theory, and novel applications of MO under uncertainty. The seminar plan will include invited talks on major topics related to uncertainty quantification, a collaborative effort to form ad-hoc working groups around central themes, several breakout sessions for in-depth discussion, and joint sessions for information sharing. Throughout the seminar, applications of MO will be emphasized. Implementation of new test problems with uncertainty and multiple objectives within popular testbeds is envisaged. Following the tradition of the recent related seminars, a special issue in a leading operations research or engineering journal on the theme of the seminar will be considered.
Creative Commons BY 4.0
Richard Allmendinger, Carlos M. Fonseca, Susan R. Hunter, and Serpil Sayin
Verwandte Seminare
- Dagstuhl-Seminar 04461: Practical Approaches to Multi-Objective Optimization (2004-11-07 - 2004-11-12) (Details)
- Dagstuhl-Seminar 06501: Practical Approaches to Multi-Objective Optimization (2006-12-10 - 2006-12-15) (Details)
- Dagstuhl-Seminar 09041: Hybrid and Robust Approaches to Multiobjective Optimization (2009-01-18 - 2009-01-23) (Details)
- Dagstuhl-Seminar 12041: Learning in Multiobjective Optimization (2012-01-22 - 2012-01-27) (Details)
- Dagstuhl-Seminar 15031: Understanding Complexity in Multiobjective Optimization (2015-01-11 - 2015-01-16) (Details)
- Dagstuhl-Seminar 18031: Personalized Multiobjective Optimization: An Analytics Perspective (2018-01-14 - 2018-01-19) (Details)
- Dagstuhl-Seminar 20031: Scalability in Multiobjective Optimization (2020-01-12 - 2020-01-17) (Details)
- Dagstuhl-Seminar 23361: Multiobjective Optimization on a Budget (2023-09-03 - 2023-09-08) (Details)
Klassifikation
- Machine Learning
- Neural and Evolutionary Computing
- Systems and Control
Schlagworte
- optimization
- simulation optimization
- evolutionary algorithms
- uncertainty quantification and handling
- decision making
