Decision Support Ecosystems: Assisted Low-Code Development of Tailored Decision Support Systems - Kirchhoff, Jonas
Erfolg und Misserfolg eines Unternehmens werden maßgeblich durch getroffene Entscheidungen beeinflusst. Daher verlassen sich Entscheider oft auf Entscheidungsunterstützungssysteme, die durch Datensimulation, -optimierung und -visualisierung bei der Identifizierung von geeigneten Entscheidungen unterstützen. Für eine optimale Unterstützung muss ein Entscheidungsunterstützungssystem (EUS) jedoch auf den Entscheidungsprozess eines Entscheiders abgestimmt sein und verfügbare Daten, Optimierungsziele, persönliche Präferenzen sowie weitere Einflussfaktoren berücksichtigen. EUS-Entwickler können aufgrund der Komplexität und Volatilität von Geschäftsumgebungen allerdings nicht alle potenziellen Entscheidungsprozesse während des Entwurfs eines EUS vorhersehen, wodurch ein EUS einem Entscheider häufig nur unzureichende Anpassungsmöglichkeiten an den individuellen Entscheidungsprozess bietet. Die Einzelanfertigung eines EUS, das auf einen Entscheidungsprozess zugeschnitten ist, ist ein kosten- und zeitintensives Unterfangen aufgrund der begrenzten Verfügbarkeit von Softwareentwicklern oder Missverständnissen zwischen Entwicklern und Entscheidern während der Entwicklung. Daher geben sich Entscheider möglicherweise mit einem handelsüblichen EUS zufrieden, das nicht vollständig mit ihrem Entscheidungsprozess übereinstimmt, suboptimale Entscheidungen begünstigt und so den Unternehmenserfolg negativ beeinflusst. In dieser Arbeit wird ein Ansatz vorgeschlagen, der es Entscheidern ermöglicht, selbst maßgeschneiderte Entscheidungsunterstützungssysteme zu entwickeln und so die Diskrepanz zwischen benötigter und tatsächlicher Entscheidungsunterstützung zu vermeiden. Dazu stellen EUS-Entwickler einen Teil der EUS-Funktionalität als wiederverwendbare Software-Dienste bereit ...

Fast Convergence of Inertial Multiobjective Gradient-like Systems with Asymptotic Vanishing Damping - Sonntag, Konstantin; Peitz, Sebastian (arXiv:2307.00975)
We present a new gradient-like dynamical system related to unconstrained convex smooth multiobjective optimization which involves inertial effects and asymptotic vanishing damping. To the best of our knowledge, this system is the first inertial gradient-like system for multiobjective optimization problems including asymptotic vanishing damping, expanding the ideas laid out in [H. Attouch and G. Garrigos, Multiobjective optimization: an inertial approach to Pareto optima, preprint, arXiv:1506.02823, 201]. We prove existence of solutions to this system in finite dimensions and further prove that its bounded solutions converge weakly to weakly Pareto optimal points. In addition, we obtain a convergence rate of order O(t−2) for the function values measured with a merit function. This approach presents a good basis for the development of fast gradient methods for multiobjective optimization.

Multiobjective Optimization of Non-Smooth PDE-Constrained Problems - Bernreuther, Marco; Dellnitz, Michael; Gebken, Bennet; Müller, Georg; Peitz, Sebastian; Sonntag, Konstantin; Volkwein, Stefan (arXiv:2308.01113)
Multiobjective optimization plays an increasingly important role in modern applications, where several criteria are often of equal importance. The task in multiobjective optimization and multiobjective optimal control is therefore to compute the set of optimal compromises (the Pareto set) between the conflicting objectives. The advances in algorithms and the increasing interest in Pareto-optimal solutions have led to a wide range of new applications related to optimal and feedback control - potentially with non-smoothness both on the level of the objectives or in the system dynamics. This results in new challenges such as dealing with expensive models (e.g., governed by partial differential equations (PDEs)) and developing dedicated algorithms handling the non-smoothness. Since in contrast to single-objective optimization, the Pareto set generally consists of an infinite number of solutions, the computational effort can quickly become challenging, which is particularly problematic when the objectives are costly to evaluate or when a solution has to be presented very quickly. This article gives an overview of recent developments in the field of multiobjective optimization of non-smooth PDE-constrained problems. In particular we report on the advances achieved within Project 2 "Multiobjective Optimization of Non-Smooth PDE-Constrained Problems - Switches, State Constraints and Model Order Reduction" of the DFG Priority Programm 1962 "Non-smooth and Complementarity-based Distributed Parameter Systems: Simulation and Hierarchical Optimization".

Pushing the Limits of Computational Combinatorial Constructions (Dagstuhl Seminar 23161) - Moura, Lucia; Nakic, Anamari; Östergård, Patric; Wassermann, Alfred; Weiß, Charlene
This report documents the program and the outcomes of Dagstuhl Seminar 23161 "Pushing the Limits of Computational Combinatorial Constructions". In this Dagstuhl Seminar, we focused on computational methods for challenging problems in combinatorial construction. This includes algorithms for construction of combinatorial objects with prescribed symmetry, for isomorph-free exhaustive generation, and for combinatorial search. Examples of specific algorithmic techniques are tactical decomposition, the Kramer-Mesner method, algebraic methods, graph isomorphism software, isomorph-free generation, clique-finding methods, heuristic search, SAT solvers, and combinatorial optimization. There was an emphasis on problems involving graphs, designs and codes, also including topics in related fields such as finite geometry, graph decomposition, Hadamard matrices, Latin squares, and q-analogs of designs and codes.
Keywords: automorphism groups; combinatorial algorithms; finite geometries; subspace designs

Optimal scheduling of a large-scale power-to-ammonia process: Effects of parameter optimization on the indirect demand response potential - Hochhaus, Thorben; Bruns, Bastian; Grünewald, Marcus; Riese, Julia (Computers & Chemical Engineering)
Keywords: Computer Science Applications; General Chemical Engineering

Numerical analysis of failure modeling in clinching process chain simulation - Bielak, Christian Roman; Böhnke, Max; Friedlein, Johannes; Bobbert, Mathias; Mergheim, Julia; Steinmann, Paul; Meschut, Gerson (Materials Research Proceedings)
Abstract. The application of the mechanical joining process clinching allows the assembly of different sheet metal materials with a wide range of material thickness configurations, which is of interest for lightweight multi-material structures. In order to be able to predict the clinched joint properties as a function of the individual manufacturing steps, current studies focus on numerical modeling of the entire clinching process chain. It is essential to be able to take into account the influence of the joining process-induced damage on the load-bearing capacity of the joint during the loading phase. This study presents a numerical damage accumulation in the clinching process based on an implemented Hosford-Coulomb failure model using a 3D clinching process model applied on the aluminum alloy EN AW-6014 in temper T4. A correspondence of the experimentally determined failure location with the element of the highest numerically determined damage accumulation is shown. Moreover, the experimentally determined failure behavior is predicted to be in agreement in the numerical loading simulation with transferred pre-damage from the process simulation.

Determination and Compensation of the Shrinkage Behavior of Cylindrical Elements in the FDM Process - Koers, Thorsten; Magyar, Balázs (Proceedings of the 34th Annual International Solid Freeform Fabrication Symposium 2023)
Fused Deposition Modeling (FDM) is an additive manufacturing process to produce complex thermoplastic geometries layer by layer. The filament is melted in a nozzle, iteratively deposited, and then cools down. Due to the solidification process, the deposited filament strands deviate from their intended position due to shrinkage, resulting in significant geometric deviations in the final part. In terms of dimensional accuracy, there is a need for optimization, especially for local curved geometries in relation to the global part with higher nominal dimensions. The aim of this study is to investigate the size and shape deviations for cylindrical FDM elements and to compensate the expected deformations by using an in-house software with adaptive scaling factors in the x-y plane. Previous studies mainly focus on simple, non-curved objects, this study also considers the influence of curvature and global as well as local deviations on the final part.

Using Reinforcement Learning for Per-Instance Algorithm Configuration on the TSP - Seiler, Moritz Vinzent; Rook, Jeroen; Heins, Jonathan; Preuß, Oliver Ludger; Bossek, Jakob; Trautmann, Heike (2023 IEEE Symposium Series on Computational Intelligence (SSCI))
Automated Algorithm Configuration (AAC) usually takes a global perspective: it identifies a parameter configuration for an (optimization) algorithm that maximizes a performance metric over a set of instances. However, the optimal choice of parameters strongly depends on the instance at hand and should thus be calculated on a per-instance basis. We explore the potential of Per-Instance Algorithm Configuration (PIAC) by using Reinforcement Learning (RL). To this end, we propose a novel PIAC approach that is based on deep neural networks. We apply it to predict configurations for the Lin– Kernighan heuristic (LKH) for the Traveling Salesperson Problem (TSP) individually for every single instance. To train our PIAC approach, we create a large set of 100000 TSP instances with 2000 nodes each — currently the largest benchmark set to the best of our knowledge. We compare our approach to the state-of-the-art AAC method Sequential Model-based Algorithm Configuration (SMAC). The results show that our PIAC approach outperforms this baseline on both the newly created instance set and established instance sets.

proDruck 3D-Druck – Technologie der Industrie 4.0 – als Mittel der Inklusion für Menschen mit Behinderungen in die Arbeitswelt - Kruse, Anne; Müller, Laura; Ott, Manuel; Koch, Rainer; Hügel, Joachim; Finke, Florian; Winter, Stephan; Gust, Thomas (Personennahe Dienstleistungen der Zukunft)

3D-Druck – Eine Technologie als Schlüssel zur Steigerung der Teilhabe - Kruse, Anne; Müller, Laura; Ott, Manuel; Jung, Philipp; Koch, Rainer; Hügel, Joachim; Finke, Florian; Winter, Stephan; Gust, Thomas (Personennahe Dienstleistungen der Zukunft)

Investigation of the Homogenization Performance of Various Faceted Mixers and Optimization with Regard to Mixing as well as Pressure Throughput Behavior - Schöppner, Volker; Frank, Maximilian

Advances In RTM Manufacturing Of Metal-FRP Hybrids By Self-Sealing And In-Mold Cleaning Techniques - Chalicheemalapalli Jayasankar, Deviprasad
The importance of fiber-reinforced plastics for lightweight construction applications is steadily increasing due to their outstanding weight-specific property values. However, a decisive disadvantage of these composite materials has so far been the high material and process costs, which is why fiber-reinforced plastics are almost exclusively used in small to medium-sized series. Optimization of manufacturing methods is of great importance to reduce the production cost. In this study, two concepts are proposed that can optimize vacuum assisted light resin transfer molding (VA-LRTM) further, leading to a possibility of fully automatic process. Conventional VA-LRTM methods are used to produce complex fiber-reinforced plastics (FRP) and hybrid components. Traditional molds used to produce components via VA-LRTM are sealed using polymer materials to prevent the leakage of matrix system. The seals undergo tremendous amounts of thermal, chemical, and mechanical loadings. Thus, sealings must be replaced in short intervals. In the current study, a concept where sealing is achieved by accelerating the curing of matrix system itself with the help of heating elements and catalysts resulting in a self-sealing approach is proposed. Another concern is mold surface contamination during component production. To address this, a modified automatic cleaning technique based on ultrasonic cleaning was proposed which can be integrated into the production line with minimum modification. Both the proposed concepts were validated and optimized using experiments, simulations, and analytical approaches by producing metal-FRP hybrid shafts.
Keywords: fiber-reinforced plastics; resin transfer molding; composites

Analyzing the Generalizability of Automated Algorithm Selection: A Case Study for Numerical Optimization - Ŝkvorc, Urban; Eftimov, Tome; Koro]ec, Peter (2023 IEEE Symposium Series on Computational Intelligence (SSCI))

A new dual matrix burner for one-dimensional investigation of aerosol flames - Apazeller, Sascha; Gonchikzhapov, Munko; Nanjaiah, Monika; Kasper, Tina; Wlokas, Irenäus; Wiggers, Hartmut; Schulz, Christof (Proceedings of the Combustion Institute)
In spray-flame synthesis of nanoparticles, a precise understanding of the reaction processes is necessary to find optimal process parameters for the formation of the desired products. Coupling the chemistries of flame, solvent, and gas-phase species initially formed from the particle precursor in combination with the complex flow geometry of the spray flame means a special challenge for the modeling of the reaction processes. A new burner has been developed that is capable to observe the reaction of precursor solutions frequently used in spray-flame synthesis. The burner provides an almost flat, laminar, and steady flame with homogeneous addition of a fine aerosol and thus enables detailed investigation and modeling of the coupled reactions inde-pendent of spray formation and turbulent mixing. With its two separate supply channel matrices, the burner also enables the use of reactants that would otherwise react with each other already before reaching the flame. These features enable the investigation of a wide range of flame-based synthesis methods for nanoparticles and, due to the flat-flame geometry, kinetics models for these processes can be developed and validated. This work describes the matrix burner development and its gas flow optimization by simulation. Droplet-size dis-tributions generated by ultrasonic nebulization and their interaction with the burner structure are investigated by phase-Doppler anemometry. As an example for nanoparticle-for ming flames from solutions, iron-oxide nanoparticle-generating flames using iron(III) nitrate nonahydrate dissolved in 1-butanol were investigated. This effort includes measurements of two-dimensional maps of the flame temperature by a thermocouple and height-dependent concentration profiles of the main species by time-of-flight mass spectrometry. Exper-imental data are compared with 1D simulations using a reduced reaction mechanism. The results show that the new burner is well suited for the development of reaction models for precursors supplied in the liquid phase usually applied in spray-flame synthesis configurations.& COPY; 2022 The Combustion Institute. Published by Elsevier Inc. All rights reserved.
Keywords: Physical and Theoretical Chemistry; Mechanical Engineering; General Chemical Engineering

Optimization of Rate-Splitting Multiple Access in Beyond Diagonal RIS-assisted URLLC Systems - Soleymani, Mohammad; Santamaria, Ignacio; Jorswieck, Eduard; Clerckx, Bruno (IEEE Transactions on Wireless Communications)
Keywords: Applied Mathematics; Electrical and Electronic Engineering; Computer Science Applications

Global smooth solutions in a three-dimensional cross-diffusive SIS epidemic model with saturated taxis at large densities - Tao, Youshan; Winkler, Michael (Evolution Equations and Control Theory)
Keywords: Applied Mathematics; Control and Optimization; Modeling and Simulation

Deriving 3D Functional Brain Regions from Multi-Slice Functional Ultrasound Data Using ICA and IVA - Lehmann, Isabell; Adali, Tülay; Kruizinga, Pieter; Hunyadi, Borbála (2023 57th Asilomar Conference on Signals, Systems, and Computers)

A bi-objective hybrid vibration damping optimization model for synchronous flow shop scheduling problems - Tavana, Madjid; Hajipour, Vahid; Alaghebandha, Mohammad; Di Caprio, Debora (Machine Learning with Applications)
Keywords: General Medicine

A novel median-based optimization model for eco-efficiency assessment in data envelopment analysis - Dellnitz, Andreas; Tavana, Madjid; Banker, Rajiv (Annals of Operations Research)
Keywords: Management Science and Operations Research; General Decision Sciences

A 28-Gb/s 27.2mW NRZ Full-Rate Bang-Bang Clock and Data Recovery in 22 nm FD-SOI CMOS Technology - Iftekhar, Mohammed; Gowda, Harshan; Kneuper, Pascal; Sadiye, Babak; Müller, Wolfgang; Scheytt, Christoph (2023 IEEE BiCMOS and Compound Semiconductor Integrated Circuits and Technology Symposium (BCICTS))