Multiobjective optimization (MO) strives for the simultaneous consideration of conflicting objectives and complicating constraints. It has become an indispensable tool in complex decision-making situations involving conflict that arises, for example, among multiple decision makers, or between multiple goals, objectives, and constraints. Applications include many areas of human activity in business, management and engineering. With the ongoing production of big amounts of data and increasing computational power, we have recently experienced a shift of paradigm from decision-making problems with relatively few variables and constraints, and two or three objective functions, towards large-scale problems involving many variables, many objective functions and constraints, and many decision makers. In view of these growing practical needs, the scalability of models and solution approaches, as a decisive characteristic for future applicability and success of multiobjective optimization, has thus become a crucial issue for two main research communities, Evolutionary Multicriterion Optimization (EMO) and Multi-Criterion Decision Making (MCDM).

In this Dagstuhl Seminar, we will focus on three main aspects of scalability in multiobjective optimization and their interplay, namely

1. MO with many objective functions,
2. MO with many decision makers, and
3. MO with many variables and large amounts of data.

This viewpoint explicitly includes problems with computationally heavy objectives, data-heavy objectives, distributed and/or hierarchical decision processes, and the interplay of these. It also brings on board parallel/distributed processing, meta-modelling, group decision making, and other related areas. Each focus will be scrutinized with regard to scalability of the methods currently at our disposal and how parallelism (many cores or GPUs) can serve this purpose. In addition to modelling and algorithm development, we strive for the development of scalable test cases and tools for numerical validation.

During the seminar, we intend to achieve the following goals by combining EMO and MCDM perspectives and tools:

(i) Understand why current approaches in multiobjective optimization do not usually scale well with (1) an increasing number of objectives, (2) an increasing number of decision makers, and (3) an increasing number of variables and data,
(ii) Jointly identify novel strategies for model building, solution algorithms, preference modelling, testing and validation that are capable of handling large-scale problems, and
(iii) Initiate collaborative projects for implementing scalable approaches in challenging real-world decision-making situations.

This seminar carries on a series of six previous Dagstuhl Seminars (04461, 06501, 09041, 12041, 15031 and 18031) that were focused on multiobjective optimization. Our major goal is to further strengthen the links between the EMO and MCDM communities, and to advance both theoretical understanding and computational techniques in multiobjective optimization.

Creative Commons BY 3.0 DE

Carlos M. Fonseca, Kathrin Klamroth, Günter Rudolph, and Margaret M. Wiecek

• Richard Allmendinger (University of Manchester, GB) [dblp]
• Mickaël Binois (INRIA - Valbonne, FR) [dblp]
• Fritz Bökler (Universität Osnabrück, DE) [dblp]
• Jürgen Branke (University of Warwick, GB) [dblp]
• Dimo Brockhoff (INRIA Saclay - Palaiseau, FR) [dblp]
• Carlos A. Coello Coello (CINVESTAV - Mexico, MX) [dblp]
• Kerstin Dächert (Fraunhofer ITWM - Kaiserslautern, DE) [dblp]
• Matthias Ehrgott (Lancaster University, GB) [dblp]
• Gabriele Eichfelder (TU Ilmenau, DE) [dblp]
• Michael Emmerich (Leiden University, NL) [dblp]
• Georges Fadel (Clemson University - Clemson, US) [dblp]
• José Rui Figueira (IST - Lisbon, PT) [dblp]
• Carlos M. Fonseca (University of Coimbra, PT) [dblp]
• Andreia P. Guerreiro (IST - Lisbon, PT) [dblp]
• Jussi Hakanen (University of Jyväskylä, FI) [dblp]
• Susan R. Hunter (Purdue University, US) [dblp]
• Hisao Ishibuchi (Southern Univ. of Science and Technology - Shenzen, CN) [dblp]
• Andrzej Jaszkiewicz (Poznan University of Technology, PL) [dblp]
• Pascal Kerschke (Universität Münster, DE) [dblp]
• Kathrin Klamroth (Universität Wuppertal, DE) [dblp]
• Karl Heinz Küfer (Fraunhofer ITWM - Kaiserslautern, DE) [dblp]
• Arnaud Liefooghe (University of Lille, FR) [dblp]
• Manuel López-Ibáñez (University of Manchester, GB) [dblp]
• Kaisa Miettinen (University of Jyväskylä, FI) [dblp]
• Sanaz Mostaghim (Universität Magdeburg, DE) [dblp]
• Boris Naujoks (TH Köln, DE) [dblp]
• Luís Paquete (University of Coimbra, PT) [dblp]
• Patrick M. Reed (Cornell University - Ithaca, US) [dblp]
• Enrico Rigoni (ESTECO SpA - Trieste, IT) [dblp]
• Günter Rudolph (TU Dortmund, DE) [dblp]
• Stefan Ruzika (TU Kaiserslautern, DE) [dblp]
• Serpil Sayin (Koc University - Istanbul, TR) [dblp]
• Anita Schöbel (Fraunhofer ITWM - Kaiserslautern, DE) [dblp]
• Britta Schulze (Universität Wuppertal, DE) [dblp]
• Pradyumn Kumar Shukla (KIT - Karlsruher Institut für Technologie, DE) [dblp]
• Ralph E. Steuer (University of Georgia, US) [dblp]
• Michael Stiglmayr (Universität Wuppertal, DE) [dblp]
• Christiane Tammer (Martin-Luther-Universität Halle-Wittenberg, DE)
• Heike Trautmann (Universität Münster, DE) [dblp]
• Tea Tusar (Jozef Stefan Institute - Ljubljana, SI) [dblp]
• Daniel Vanderpooten (University Paris-Dauphine, FR) [dblp]
• Margaret M. Wiecek (Clemson University, US) [dblp]