The purpose of multiobjective optimization is to develop methods that can solve problems having a number of (conflicting) optimization criteria and constraints, providing a multitude of solution alternatives, rather than pursuing only one "optimal" solution. In this aim the field is highly successful: its methods have a track record of improving decision making across a broad swath of applications, indeed wherever there are conflicting goals or objectives. Yet, since multiobjective optimization has focused almost exclusively on serving a single "decision maker", providing solutions merely as potential (not actual) alternatives, it is not presently a technology that can serve mass markets with mass solutions. A new approach is needed if we are to fulfil the demanding aims of mass-customization, product/service variation and personalization we see today in areas such as engineering, planning, operations, investment, media and Web services, and healthcare. Taking the first steps, this Dagstuhl Seminar will explore an “Analytics” perspective already proven in handling large-scale pervasive data, and seek to build the scientific foundations for delivering efficient and effective (even optimal) mass-personalization.

The seminar will be organized around three application challenges which distinguish between different ways that personalization can be needed or delivered in an optimization and decision-making setting. These are

1. platform design and product lines,
2. responsive and online personalization, and
3. complex networks of decision makers.

All three topics provide a number of challenges in common, as well as distinctive aspects, and all will benefit from the input of researchers from a variety of backgrounds and interests across Evolutionary Multiobjective Optimization (EMO) and Multiple Criteria Decision Making (MCDM).

During this Dagstuhl Seminar, the three application challenges will be crosslinked with three research domains that constitute the methodological core of multiobjective optimization and have been the foundation for the discussions at the previous Dagstuhl Seminars.

1. Model building, approximation, and representation comprising theoretical aspects, interwoven systems, set based optimization, interrelating decision space and objective space, robustness and noise handling, and analytics.
2. Preference modelling comprising problem structuring and formulation, preference elicitation and learning, preference models and trade-offs, group decision making, high-dimensional problems, interwoven systems, and analytics.
3. Algorithm design and efficiency comprising complexity, handling very large data, high-dimensional problems (many objective functions), evolutionary vs exact approaches (and hybrids), and analytics.

This Seminar carries on a series of five previous Dagstuhl Seminars (04461, 06501, 09041, 12041 and 15031) 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

Kathrin Klamroth, Joshua D. Knowles, Günter Rudolph, and Margaret M. Wiecek

• Richard Allmendinger (University of Manchester, GB) [dblp]
• Mickaël Binois (Argonne National Laboratory - Lemont, US) [dblp]
• Jürgen Branke (University of Warwick, GB) [dblp]
• Dimo Brockhoff (INRIA Saclay - Palaiseau, FR) [dblp]
• Roberto Calandra (University of California - Berkeley, US) [dblp]
• Carlos A. Coello Coello (CINVESTAV - Mexico, MX) [dblp]
• Kerstin Dächert (Universität Wuppertal, DE) [dblp]
• Kalyanmoy Deb (Michigan State University, US) [dblp]
• Matthias Ehrgott (Lancaster University Management School, GB) [dblp]
• Gabriele Eichfelder (TU Ilmenau, DE) [dblp]
• Michael Emmerich (Leiden University, NL) [dblp]
• Alexander Engau (Lancaster University Management School, GB) [dblp]
• Georges Fadel (Clemson University - Clemson, US) [dblp]
• José Rui Figueira (IST - Lisbon, PT) [dblp]
• Carlos M. Fonseca (University of Coimbra, PT) [dblp]
• Abhinav Gaur (Michigan State University - East Lansing, US) [dblp]
• Salvatore Greco (University of Catania, IT) [dblp]
• Jussi Hakanen (University of Jyväskylä, FI) [dblp]
• Johannes Jahn (Universität Erlangen-Nürnberg, DE) [dblp]
• Andrzej Jaszkiewicz (Poznan University of Technology, PL) [dblp]
• Milosz Kadzinski (Poznan University of Technology, PL) [dblp]
• Kathrin Klamroth (Universität Wuppertal, DE) [dblp]
• Karl Heinz Küfer (Fraunhofer ITWM - Kaiserslautern, DE) [dblp]
• Christoph Lofi (TU Delft, NL) [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]
• Frank Neumann (University of Adelaide, AU) [dblp]
• Luís Paquete (University of Coimbra, PT) [dblp]
• Robin Purshouse (University of Sheffield, GB) [dblp]
• Patrick M. Reed (Cornell University, US) [dblp]
• Günter Rudolph (TU Dortmund, DE) [dblp]
• Stefan Ruzika (TU Kaiserslautern, DE) [dblp]
• Serpil Sayin (Koc University - Istanbul, TR) [dblp]
• Pradyumn Kumar Shukla (KIT - Karlsruher Institut für Technologie, DE) [dblp]
• Roman Slowinski (Poznan University of Technology, PL) [dblp]
• Ralph E. Steuer (University of Georgia, US) [dblp]
• Theodor J. Stewart (University of Cape Town, ZA) [dblp]
• Michael Stiglmayr (Universität Wuppertal, DE) [dblp]
• Lothar Thiele (ETH Zürich, CH) [dblp]
• Selvakumar Ulaganathan (Noesis Solutions - Leuven, BE) [dblp]
• Daniel Vanderpooten (University Paris-Dauphine, FR) [dblp]
• Margaret M. Wiecek (Clemson University, US) [dblp]