Guest Editors: Monica Cojocaru and Tamas Terlaky
Paper submission deadline: 31 January, 2020
Jussi Hakanen (Faculty of Information Technology, University of Jyväskylä)
Richard Allmendinger (Alliance Manchester Business School, University of Manchester, UK)
Aim and scope
The emergence of advanced technologies and digitalization has been changing our world and leading to a situation where physical assets are being augmented heavily with non-physical assets. In addition to widely used simulation-based optimization, data-driven optimization approaches have become more popular in engineering sciences due to availability of large amounts of data collected in every field (e.g. from IoT, sensors, experimental measurements etc.). Therefore, the effective combination of data and advanced engineering and management technologies and skills is becoming a key asset to a company urging the need to rethink how to tackle modern decision making problems. The consideration of various competing factors related to business, technical, workforce, safety and environmental aspects further increases the complexity of decision making and leads to multiple criteria decision making (MCDM) problems. This special issue focuses on the intersection between Engineering, Data Science, Multiple Criteria Decision Making (MCDM) and Multiobjective Optimization (MO). The development of new models and algorithmic methods to solve such problems is in the focus as much as the application of these concepts to real problems.
The aim of the issue is to bring together academics and practitioners with different expertise, in particular, in engineering, computer science, mathematics, data science and business.
This special issue is connected to but not restricted to papers presented at the 25th International Conference on Multiple Criteria Decision Making, MCDM2019 (to be held in Istanbul, Turkey, in June 2019).
Major topics of interest
All submissions related to the development/application of multi/many-objective optimization in engineering are welcome. Especially, submissions considering novel approaches combining engineering and data science are highly encouraged. Topics of interest (but not limited to) include:
Papers should be submitted online at https://www.opte-journal.com and the submission deadline for full-length papers is January 31, 2020. Upon manuscript submission, please select the special issue "MCDM 2019". Submissions will be peer-reviewed according to the standards of the journal. Manuscripts will be processed as they arrive and will be published online as soon as they are accepted. For planning purposes, interested authors are encouraged to email a tentative title to the guest editors.
Please feel free to contact us in case you have any questions
Jussi Hakanen: firstname.lastname@example.org <mailto:email@example.com>
Richard Allmendinger: firstname.lastname@example.org <mailto:email@example.com>
Michael Ulbrich, Professor, Chair of Mathematical Optimization, Department of Mathematics, Technical University of Munich ⟨firstname.lastname@example.org⟩.
Boris Vexler, Professor, Chair of Optimal Control, Department of Mathematics, Technical University of Munich ⟨email@example.com⟩.
This call aims at publishing research work on PDE-constrained optimization connected to applications in engineering. The call is open to all types of papers, including theoretical, applied, and algorithmic articles, or combinations of them.
The accurate modeling of complex physical and technical systems heavily relies on PDEs. The resulting systems live in infinite-dimensional function spaces and can involve nonlinearity, nonsmoothness, or uncertainty. Optimization with PDE constraints is the enabling discipline for analyzing and solving highly important problem classes connected to these systems, such as: shape and topology optimization, optimal control, inverse problems, parameter identification, etc. Beyond more traditional applications, the field is increasingly interacting with other timely and important areas, such as uncertainty quantification, data science, or mathematical imaging. Studying the theoretical and numerical aspects of PDE-constrained optimization problems in their original function space setting and tying the developments closely to the latest theoretical and computational advances for PDEs are key elements for a strong theory and for robust, mesh-independent solvers. Mathematically, PDE-constrained optimization is as rich as its numerous applications: It combines theoretical and practical methodology from optimization, PDEs, functional analysis, nonsmooth and variational analysis, numerical analysis, and scientific computing; it also can involve probability and measure theory.
This special issues targets at showcasing the latest advances in PDE-constrained optimization at the intersection of mathematics and engineering applications.
Please submit to the Optimization and Engineering (OPTE) journal at https://www.springer.com/mathematics/journal/11081 and select special issue “SI: PDE 2019”. All sub- missions must be original and may not be under review by another publication. Interested authors should consult the journal’s “Instructions for Authors”, at http://www.springer.com/ mathematics/journal/11081. All submitted papers will be reviewed on a peer review basis as soon as they are received. Accepted papers will be available at Online First until the complete Special Issue appears.
All inquiries should be directed to the attention of:
Michael Ulbrich, Subject Editor and Guest Editor (firstname.lastname@example.org)
Boris Vexler, Guest Editor (email@example.com)
Optimization and Engineering (OPTE) journal