@article{32558, abstract = {{With the rapid progress of technological development, self-efficacy in reference to digital devices (i.e., information and computer technology [ICT] self-efficacy) is an important driver that helps students to deal with technological problems and support their lifelong learning processes. Schools, peers, and home learning environments are important sources for the development of positive self-efficacy. Expanding on previous research, we investigated the associations between different aspects of the digital home learning environment and students’ ICT self-efficacy. The moderation effects of gender were also tested. A total of 651 children answered a questionnaire about different digital home learning environment dimensions and estimated their ICT self-efficacy using an adapted scale—Schwarzer and Jerusalem’s (1999) general self-efficacy scale. Using the structural equation modeling technique, a digital home learning environment containing six different qualities of parental support was investigated. Families’ cultural capital, parents’ attitudes toward the Internet, and shared Internet activities at home contributed positively to ICT self-efficacy. We observed small gender differences, with the moderation effect being nonsignificant. The results help researchers and practitioners to understand how different dimensions of the digital home learning environment support ICT self-efficacy. We will discuss how parents can enhance the home learning environment and how teachers can integrate this knowledge into formal education.}}, author = {{Bonanati, Sabrina and Buhl, Heike M.}}, issn = {{1387-1579}}, journal = {{Learning Environments Research}}, keywords = {{Digital media use, Gender, Home learning environment, ICT self-efcacy, Motivation, Parental involvement}}, number = {{2}}, pages = {{485--505}}, publisher = {{Springer Science and Business Media LLC}}, title = {{{The digital home learning environment and its relation to children’s ICT self-efficacy}}}, doi = {{10.1007/s10984-021-09377-8}}, volume = {{25}}, year = {{2021}}, } @inproceedings{19609, abstract = {{Modern services comprise interconnected components, e.g., microservices in a service mesh, that can scale and run on multiple nodes across the network on demand. To process incoming traffic, service components have to be instantiated and traffic assigned to these instances, taking capacities and changing demands into account. This challenge is usually solved with custom approaches designed by experts. While this typically works well for the considered scenario, the models often rely on unrealistic assumptions or on knowledge that is not available in practice (e.g., a priori knowledge). We propose a novel deep reinforcement learning approach that learns how to best coordinate services and is geared towards realistic assumptions. It interacts with the network and relies on available, possibly delayed monitoring information. Rather than defining a complex model or an algorithm how to achieve an objective, our model-free approach adapts to various objectives and traffic patterns. An agent is trained offline without expert knowledge and then applied online with minimal overhead. Compared to a state-of-the-art heuristic, it significantly improves flow throughput and overall network utility on real-world network topologies and traffic traces. It also learns to optimize different objectives, generalizes to scenarios with unseen, stochastic traffic patterns, and scales to large real-world networks.}}, author = {{Schneider, Stefan Balthasar and Manzoor, Adnan and Qarawlus, Haydar and Schellenberg, Rafael and Karl, Holger and Khalili, Ramin and Hecker, Artur}}, booktitle = {{IEEE International Conference on Network and Service Management (CNSM)}}, keywords = {{self-driving networks, self-learning, network coordination, service coordination, reinforcement learning, deep learning, nfv}}, publisher = {{IEEE}}, title = {{{Self-Driving Network and Service Coordination Using Deep Reinforcement Learning}}}, year = {{2020}}, } @inproceedings{17370, abstract = {{ We consider a natural extension to the metric uncapacitated Facility Location Problem (FLP) in which requests ask for different commodities out of a finite set \( S \) of commodities. Ravi and Sinha (SODA 2004) introduced the model as the \emph{Multi-Commodity Facility Location Problem} (MFLP) and considered it an offline optimization problem. The model itself is similar to the FLP: i.e., requests are located at points of a finite metric space and the task of an algorithm is to construct facilities and assign requests to facilities while minimizing the construction cost and the sum over all assignment distances. In addition, requests and facilities are heterogeneous; they request or offer multiple commodities out of $S$. A request has to be connected to a set of facilities jointly offering the commodities demanded by it. In comparison to the FLP, an algorithm has to decide not only if and where to place facilities, but also which commodities to offer at each. To the best of our knowledge we are the first to study the problem in its online variant in which requests, their positions and their commodities are not known beforehand but revealed over time. We present results regarding the competitive ratio. On the one hand, we show that heterogeneity influences the competitive ratio by developing a lower bound on the competitive ratio for any randomized online algorithm of \( \Omega ( \sqrt{|S|} + \frac{\log n}{\log \log n} ) \) that already holds for simple line metrics. Here, \( n \) is the number of requests. On the other side, we establish a deterministic \( \mathcal{O}(\sqrt{|S|} \cdot \log n) \)-competitive algorithm and a randomized \( \mathcal{O}(\sqrt{|S|} \cdot \frac{\log n}{\log \log n} ) \)-competitive algorithm. Further, we show that when considering a more special class of cost functions for the construction cost of a facility, the competitive ratio decreases given by our deterministic algorithm depending on the function.}}, author = {{Castenow, Jannik and Feldkord, Björn and Knollmann, Till and Malatyali, Manuel and Meyer auf der Heide, Friedhelm}}, booktitle = {{Proceedings of the 32nd ACM Symposium on Parallelism in Algorithms and Architectures}}, isbn = {{9781450369350}}, keywords = {{Online Multi-Commodity Facility Location, Competitive Ratio, Online Optimization, Facility Location Problem}}, title = {{{The Online Multi-Commodity Facility Location Problem}}}, doi = {{10.1145/3350755.3400281}}, year = {{2020}}, } @article{20143, author = {{Otroshi, Mortaza and Rossel, Moritz and Meschut, Gerson}}, journal = {{Journal of Advanced Joining Processes}}, keywords = {{Self-pierce riveting, Ductile fracture, Damage modeling, GISSMO damage model}}, publisher = {{Elsevier}}, title = {{{Stress state dependent damage modeling of self-pierce riveting process simulation using GISSMO damage model}}}, doi = {{10.1016/j.jajp.2020.100015}}, volume = {{1}}, year = {{2020}}, } @article{35298, abstract = {{Im Artikel werden drei verschiedene Lernzugänge (kom-petenzorientiertes, ästhetisches und biographisches Lernen) vorgestellt und aus theoretischer Perspektive deren motivierender Gehalt für selbstreguliertes Lernen in Praxisphasen des Lehramtsstudiumsherausgearbeitet. Als theoretische Grund-lage dient die Selbstbestimmungstheorie als zentrale motivationale Theorie zur Erklärung selbstbestimmten Handelns.}}, author = {{Caruso, Carina and Adammek, Christine and Bonanati, Sabrina and Wiescholek, Sybille}}, issn = {{2625-0675}}, journal = {{Herausforderung Lehrer*innenbildung - Zeitschrift Zur Konzeption, Gestaltung Und Diskussion}}, keywords = {{ästhetische Forschung, Biographiearbeit, Praxissemester, Professionalisierung, selbstreguliertes Lernen, Motivation / aesthetic research, biographical work, long-term internship, profes-sionalization, self-regulated learning, motivation}}, number = {{1}}, pages = {{18--33}}, title = {{{Motivierende Lernzugänge als Ausgangspunkt der Professionalisierung angehender Lehrer_innen}}}, doi = {{10.4119/hlz-2540}}, volume = {{3}}, year = {{2020}}, } @inproceedings{48847, abstract = {{Dynamic optimization problems have gained significant attention in evolutionary computation as evolutionary algorithms (EAs) can easily adapt to changing environments. We show that EAs can solve the graph coloring problem for bipartite graphs more efficiently by using dynamic optimization. In our approach the graph instance is given incrementally such that the EA can reoptimize its coloring when a new edge introduces a conflict. We show that, when edges are inserted in a way that preserves graph connectivity, Randomized Local Search (RLS) efficiently finds a proper 2-coloring for all bipartite graphs. This includes graphs for which RLS and other EAs need exponential expected time in a static optimization scenario. We investigate different ways of building up the graph by popular graph traversals such as breadth-first-search and depth-first-search and analyse the resulting runtime behavior. We further show that offspring populations (e. g. a (1 + {$\lambda$}) RLS) lead to an exponential speedup in {$\lambda$}. Finally, an island model using 3 islands succeeds in an optimal time of {$\Theta$}(m) on every m-edge bipartite graph, outperforming offspring populations. This is the first example where an island model guarantees a speedup that is not bounded in the number of islands.}}, author = {{Bossek, Jakob and Neumann, Frank and Peng, Pan and Sudholt, Dirk}}, booktitle = {{Proceedings of the Genetic and Evolutionary Computation Conference}}, isbn = {{978-1-4503-7128-5}}, keywords = {{dynamic optimization, evolutionary algorithms, running time analysis, theory}}, pages = {{1277–1285}}, publisher = {{Association for Computing Machinery}}, title = {{{More Effective Randomized Search Heuristics for Graph Coloring through Dynamic Optimization}}}, doi = {{10.1145/3377930.3390174}}, year = {{2020}}, } @inproceedings{48849, abstract = {{One-shot optimization tasks require to determine the set of solution candidates prior to their evaluation, i.e., without possibility for adaptive sampling. We consider two variants, classic one-shot optimization (where our aim is to find at least one solution of high quality) and one-shot regression (where the goal is to fit a model that resembles the true problem as well as possible). For both tasks it seems intuitive that well-distributed samples should perform better than uniform or grid-based samples, since they show a better coverage of the decision space. In practice, quasi-random designs such as Latin Hypercube Samples and low-discrepancy point sets are indeed very commonly used designs for one-shot optimization tasks. We study in this work how well low star discrepancy correlates with performance in one-shot optimization. Our results confirm an advantage of low-discrepancy designs, but also indicate the correlation between discrepancy values and overall performance is rather weak. We then demonstrate that commonly used designs may be far from optimal. More precisely, we evolve 24 very specific designs that each achieve good performance on one of our benchmark problems. Interestingly, we find that these specifically designed samples yield surprisingly good performance across the whole benchmark set. Our results therefore give strong indication that significant performance gains over state-of-the-art one-shot sampling techniques are possible, and that evolutionary algorithms can be an efficient means to evolve these.}}, author = {{Bossek, Jakob and Doerr, Carola and Kerschke, Pascal and Neumann, Aneta and Neumann, Frank}}, booktitle = {{Parallel Problem Solving from Nature (PPSN XVI)}}, isbn = {{978-3-030-58111-4}}, keywords = {{Continuous optimization, Fully parallel search, One-shot optimization, Regression, Surrogate-assisted optimization}}, pages = {{111–124}}, publisher = {{Springer-Verlag}}, title = {{{Evolving Sampling Strategies for One-Shot Optimization Tasks}}}, doi = {{10.1007/978-3-030-58112-1_8}}, year = {{2020}}, } @inproceedings{48851, abstract = {{Several important optimization problems in the area of vehicle routing can be seen as variants of the classical Traveling Salesperson Problem (TSP). In the area of evolutionary computation, the Traveling Thief Problem (TTP) has gained increasing interest over the last 5 years. In this paper, we investigate the effect of weights on such problems, in the sense that the cost of traveling increases with respect to the weights of nodes already visited during a tour. This provides abstractions of important TSP variants such as the Traveling Thief Problem and time dependent TSP variants, and allows to study precisely the increase in difficulty caused by weight dependence. We provide a 3.59-approximation for this weight dependent version of TSP with metric distances and bounded positive weights. Furthermore, we conduct experimental investigations for simple randomized local search with classical mutation operators and two variants of the state-of-the-art evolutionary algorithm EAX adapted to the weighted TSP. Our results show the impact of the node weights on the position of the nodes in the resulting tour.}}, author = {{Bossek, Jakob and Casel, Katrin and Kerschke, Pascal and Neumann, Frank}}, booktitle = {{Proceedings of the Genetic and Evolutionary Computation Conference}}, isbn = {{978-1-4503-7128-5}}, keywords = {{dynamic optimization, evolutionary algorithms, running time analysis, theory}}, pages = {{1286–1294}}, publisher = {{Association for Computing Machinery}}, title = {{{The Node Weight Dependent Traveling Salesperson Problem: Approximation Algorithms and Randomized Search Heuristics}}}, doi = {{10.1145/3377930.3390243}}, year = {{2020}}, } @inproceedings{48845, abstract = {{In practice, e.g. in delivery and service scenarios, Vehicle-Routing-Problems (VRPs) often imply repeated decision making on dynamic customer requests. As in classical VRPs, tours have to be planned short while the number of serviced customers has to be maximized at the same time resulting in a multi-objective problem. Beyond that, however, dynamic requests lead to the need for re-planning of not yet realized tour parts, while already realized tour parts are irreversible. In this paper we study this type of bi-objective dynamic VRP including sequential decision making and concurrent realization of decisions. We adopt a recently proposed Dynamic Evolutionary Multi-Objective Algorithm (DEMOA) for a related VRP problem and extend it to the more realistic (here considered) scenario of multiple vehicles. We empirically show that our DEMOA is competitive with a multi-vehicle offline and clairvoyant variant of the proposed DEMOA as well as with the dynamic single-vehicle approach proposed earlier.}}, author = {{Bossek, Jakob and Grimme, Christian and Trautmann, Heike}}, booktitle = {{Proceedings of the Genetic and Evolutionary Computation Conference}}, isbn = {{978-1-4503-7128-5}}, keywords = {{decision making, dynamic optimization, evolutionary algorithms, multi-objective optimization, vehicle routing}}, pages = {{166–174}}, publisher = {{Association for Computing Machinery}}, title = {{{Dynamic Bi-Objective Routing of Multiple Vehicles}}}, doi = {{10.1145/3377930.3390146}}, year = {{2020}}, } @inproceedings{48850, abstract = {{Sequential model-based optimization (SMBO) approaches are algorithms for solving problems that require computationally or otherwise expensive function evaluations. The key design principle of SMBO is a substitution of the true objective function by a surrogate, which is used to propose the point(s) to be evaluated next. SMBO algorithms are intrinsically modular, leaving the user with many important design choices. Significant research efforts go into understanding which settings perform best for which type of problems. Most works, however, focus on the choice of the model, the acquisition function, and the strategy used to optimize the latter. The choice of the initial sampling strategy, however, receives much less attention. Not surprisingly, quite diverging recommendations can be found in the literature. We analyze in this work how the size and the distribution of the initial sample influences the overall quality of the efficient global optimization (EGO) algorithm, a well-known SMBO approach. While, overall, small initial budgets using Halton sampling seem preferable, we also observe that the performance landscape is rather unstructured. We furthermore identify several situations in which EGO performs unfavorably against random sampling. Both observations indicate that an adaptive SMBO design could be beneficial, making SMBO an interesting test-bed for automated algorithm design.}}, author = {{Bossek, Jakob and Doerr, Carola and Kerschke, Pascal}}, booktitle = {{Proceedings of the Genetic and Evolutionary Computation Conference}}, isbn = {{978-1-4503-7128-5}}, keywords = {{continuous black-box optimization, design of experiments, initial design, sequential model-based optimization}}, pages = {{778–786}}, publisher = {{Association for Computing Machinery}}, title = {{{Initial Design Strategies and Their Effects on Sequential Model-Based Optimization: An Exploratory Case Study Based on BBOB}}}, doi = {{10.1145/3377930.3390155}}, year = {{2020}}, } @article{48848, abstract = {{We build upon a recently proposed multi-objective view onto performance measurement of single-objective stochastic solvers. The trade-off between the fraction of failed runs and the mean runtime of successful runs \textendash both to be minimized \textendash is directly analyzed based on a study on algorithm selection of inexact state-of-the-art solvers for the famous Traveling Salesperson Problem (TSP). Moreover, we adopt the hypervolume indicator (HV) commonly used in multi-objective optimization for simultaneously assessing both conflicting objectives and investigate relations to commonly used performance indicators, both theoretically and empirically. Next to Penalized Average Runtime (PAR) and Penalized Quantile Runtime (PQR), the HV measure is used as a core concept within the construction of per-instance algorithm selection models offering interesting insights into complementary behavior of inexact TSP solvers. \textbullet The multi-objective perspective is naturally generalizable to multiple objectives. \textbullet Proof of relationship between HV and the PAR in the considered bi-objective space. \textbullet New insights into complementary behavior of stochastic optimization algorithms.}}, author = {{Bossek, Jakob and Kerschke, Pascal and Trautmann, Heike}}, issn = {{1568-4946}}, journal = {{Applied Soft Computing}}, keywords = {{Algorithm selection, Combinatorial optimization, Multi-objective optimization, Performance measurement, Traveling Salesperson Problem}}, number = {{C}}, title = {{{A Multi-Objective Perspective on Performance Assessment and Automated Selection of Single-Objective Optimization Algorithms}}}, doi = {{10.1016/j.asoc.2019.105901}}, volume = {{88}}, year = {{2020}}, } @article{46334, abstract = {{We build upon a recently proposed multi-objective view onto performance measurement of single-objective stochastic solvers. The trade-off between the fraction of failed runs and the mean runtime of successful runs – both to be minimized – is directly analyzed based on a study on algorithm selection of inexact state-of-the-art solvers for the famous Traveling Salesperson Problem (TSP). Moreover, we adopt the hypervolume indicator (HV) commonly used in multi-objective optimization for simultaneously assessing both conflicting objectives and investigate relations to commonly used performance indicators, both theoretically and empirically. Next to Penalized Average Runtime (PAR) and Penalized Quantile Runtime (PQR), the HV measure is used as a core concept within the construction of per-instance algorithm selection models offering interesting insights into complementary behavior of inexact TSP solvers.}}, author = {{Bossek, Jakob and Kerschke, Pascal and Trautmann, Heike}}, issn = {{1568-4946}}, journal = {{Applied Soft Computing}}, keywords = {{Algorithm selection, Multi-objective optimization, Performance measurement, Combinatorial optimization, Traveling Salesperson Problem}}, pages = {{105901}}, title = {{{A multi-objective perspective on performance assessment and automated selection of single-objective optimization algorithms}}}, doi = {{https://doi.org/10.1016/j.asoc.2019.105901}}, volume = {{88}}, year = {{2020}}, } @article{33866, abstract = {{Helhmoltz–Kirchhoff equations of motions of vortices of an incompressible fluid in the plane define a dynamics with singularities and this leads to a Zermelo navigation problem describing the ship travel in such a field where the control is the heading angle. Considering one vortex, we define a time minimization problem which can be analyzed with the technics of geometric optimal control combined with numerical simulations, the geometric frame being the extension of Randers metrics in the punctured plane, with rotational symmetry. Candidates as minimizers are parameterized thanks to the Pontryagin Maximum Principle as extremal solutions of a Hamiltonian vector field. We analyze the time minimal solution to transfer the ship between two points where during the transfer the ship can be either in a strong current region in the vicinity of the vortex or in a weak current region. The analysis is based on a micro-local classification of the extremals using mainly the integrability properties of the dynamics due to the rotational symmetry. The discussion is complex and related to the existence of an isolated extremal (Reeb) circle due to the vortex singularity. The explicit computation of cut points where the extremal curves cease to be optimal is given and the spheres are described in the case where at the initial point the current is weak.}}, author = {{Bonnard, Bernard and Cots, Olivier and Wembe Moafo, Boris Edgar}}, issn = {{1292-8119}}, journal = {{ESAIM: Control, Optimisation and Calculus of Variations}}, keywords = {{Computational Mathematics, Control and Optimization, Control and Systems Engineering}}, publisher = {{EDP Sciences}}, title = {{{A Zermelo navigation problem with a vortex singularity}}}, doi = {{10.1051/cocv/2020058}}, volume = {{27}}, year = {{2020}}, } @proceedings{19976, abstract = {{The aim to reduce pollutant emission has led to a trend towards lightweight construction in car body development during the last years. As a consequence of the resulting need for multi-material design, mechanical joining technologies become increasingly important. Mechanical joining allows for the combination of dissimilar materials, while thermic joining techniques reach their limits. Self-piercing riveting enables the joining of dissimilar materials by using semi-tubular rivets as mechanical fasteners. The rivet production, however, is costly and time-consuming, as the rivets generally have to be hardened, tempered and coated after forming, in order to achieve an adequate strength and corrosion resistance. A promising approach to improve the efficiency of the rivet manufacturing is the use of high-strength high nitrogen steel as rivet material because these additional process steps would not be necessary anymore. As a result of the comparatively high nitrogen content, such steels have various beneficial properties like higher strength, good ductility and improved corrosion resistance. By cold bulk forming of high nitrogen steels high-strength parts can be manufactured due to the strengthening which is caused by the high strain hardening. However, high tool loads thereby have to be expected and are a major challenge during the production process. Consequently, there is a need for appropriate forming strategies. This paper presents key aspects concerning the process design for the manufacturing of semi-tubular self-piercing rivets made of high-strength steel. The aim is to produce the rivets in several forming stages without intermediate heat treatment between the single stages. Due to the high strain hardening of the material, a two stage forming concept will be investigated. Cup-backward extrusion is chosen as the first process step in order to form the rivet shank without forming the rivet foot. Thus, the strain hardening effects in the area of the rivet foot are minimized and the tool loads during the following process step can be reduced. During the second and final forming stage the detailed geometry of the rivet foot and the rivet head is formed. In this context, the effect of different variations, for example concerning the final geometry of the rivet foot, on the tool load is investigated using multistage numerical analysis. Furthermore, the influence of the process temperature on occurring stresses is analysed. Based on the results of the investigations, an adequate forming strategy and a tool concept for the manufacturing of semi-tubular self-piercing rivets made of high-strength steel are presented.}}, editor = {{Kuball, Clara-Maria and Uhe, Benedikt and Meschut, Gerson and Merklein, Marion}}, keywords = {{high nitrogen steel, self-piercing riveting, joining by forming, bulk forming, tool design}}, pages = {{280--285}}, title = {{{Process design for the forming of semi-tubular self-piercing rivets made of high nitrogen steel}}}, doi = {{10.1016/j.promfg.2020.08.052}}, volume = {{50}}, year = {{2020}}, } @article{19973, abstract = {{As a result of lightweight design, increased use is being made of high-strength steel and aluminium in car bodies. Self-piercing riveting is an established technique for joining these materials. The dissimilar properties of the two materials have led to a number of different rivet geometries in the past. Each rivet geometry fulfils the requirements of the materials within a limited range. In the present investigation, an improved rivet geometry is developed, which permits the reliable joining of two material combinations that could only be joined by two different rivet geometries up until now. Material combination 1 consists of high-strength steel on both sides, while material combination 2 comprises aluminium on the punch side and high-strength steel on the die side. The material flow and the stress and strain conditions prevailing during the joining process are analysed by means of numerical simulation. The rivet geometry is then improved step-by-step on the basis of this analysis. Finally, the improved rivet geometry is manufactured and the findings of the investigation are verified in experimental joining tests.}}, author = {{Uhe, Benedikt and Kuball, Clara-Maria and Merklein, Marion and Meschut, Gerson}}, journal = {{Production Engineering}}, keywords = {{Self-piercing riveting, Joining technology, Rivet geometry, Multi-material design, High-strength steel, Aluminium}}, pages = {{417--423}}, title = {{{Improvement of a rivet geometry for the self-piercing riveting of high-strength steel and multi-material joints}}}, doi = {{10.1007/s11740-020-00973-w}}, volume = {{14}}, year = {{2020}}, } @proceedings{19974, abstract = {{Due to the trend towards lightweight design in car body development mechanical joining technologies become increasingly important. These techniques allow for the joining of dissimilar materials and thus enable multi-material design, while thermic joining methods reach their limits. Semi-tubular self-piercing riveting is an important mechanical joining technology. The rivet production, however, is costly and time-consuming, as the process consists of several process steps including the heat treatment and coating of the rivets in order to achieve an adequate strength and corrosion resistance. The use of high nitrogen steel as rivet material leads to the possibility of reducing process steps and hence increasing the efficiency of the process. However, the high tool loads being expected due to the high strain hardening of the material are a major challenge during the rivet production. Thus, there is a need for appropriate forming strategies, such as the manufacturing of the rivets at elevated temperatures. Prior investigations led to the conclusion that forming already at 200 °C results in a distinct reduction of the yield strength. To create a deeper understanding of the forming behaviour of high nitrogen steel at elevated temperatures, compression tests were conducted in a temperature range between room temperature and 200 °C. The determined true stress – true strain curves are the basis for the further process and tool design of the rivet production. Another key factor for the rivet manufacturing at elevated temperatures is the influence of the process temperature on the tribological conditions. For this reason, ring compression tests at room temperature and 200 °C are carried out. The friction factors are determined on the basis of calibration curves resulting from the numerical analysis of the ring compression process. The investigations indicate that the friction factor at 200 °C is significantly higher compared to room temperature. This essential fact has to be taken into account for the process and tool design for the rivet production using high nitrogen steel.}}, editor = {{Kuball, Clara-Maria and Jung, R and Uhe, Benedikt and Meschut, Gerson and Merklein, Marion}}, keywords = {{High nitrogen steel, Self-piercing riveting, Joining by forming, Bulk forming, Strain hardening}}, title = {{{Influence of the process temperature on the forming behaviour and the friction during bulk forming of high nitrogen steel}}}, doi = {{10.1016/j.jajp.2020.100023}}, volume = {{1}}, year = {{2020}}, } @article{4562, abstract = {{Employing main and sector-specific investment-grade CDS indices from the North American and European CDS market and performing mean-variance out-of-sample analyses for conservative and aggressive investors over the period from 2006 to 2014, this paper analyzes portfolio benefits of adding corporate CDS indices to a traditional financial portfolio consisting of stock and sovereign bond indices. As a baseline result, we initially find an increase in portfolio (downside) risk-diversification when adding CDS indices, which is observed irrespective of both CDS markets, investor-types and different sub-periods, including the global financial crisis and European sovereign debt crisis. In addition, the analysis reveals higher portfolio excess returns and performance in CDS index portfolios, however, these effects clearly differ between markets, investor-types and sub-periods. Overall, portfolio benefits of adding CDS indices mainly result from the fact that institutional investors replace sovereign bond indices rather than stock indices by CDS indices due to better risk-return characteristics. Our baseline findings remain robust under a variety of robustness checks. Results from sensitivity analyses provide further important implications for institutional investors with a strategic focus on a long-term conservative portfolio management.}}, author = {{Hippert, Benjamin and Uhde, André and Wengerek, Sascha Tobias}}, journal = {{Review of Derivatives Research }}, keywords = {{Corporate credit default swap indices, Mean-variance asset allocation, Out-of-sample portfolio optimization, Portfolio risk-diversification, Portfolio performance evaluation}}, number = {{2}}, pages = {{203--259}}, title = {{{Portfolio Benefits of Adding Corporate Credit Default Swap Indices: Evidence from North America and Europe}}}, doi = {{https://doi.org/10.1007/s11147-018-9148-8}}, volume = {{22}}, year = {{2019}}, } @inproceedings{48841, abstract = {{We tackle a bi-objective dynamic orienteering problem where customer requests arise as time passes by. The goal is to minimize the tour length traveled by a single delivery vehicle while simultaneously keeping the number of dismissed dynamic customers to a minimum. We propose a dynamic Evolutionary Multi-Objective Algorithm which is grounded on insights gained from a previous series of work on an a-posteriori version of the problem, where all request times are known in advance. In our experiments, we simulate different decision maker strategies and evaluate the development of the Pareto-front approximations on exemplary problem instances. It turns out, that despite severely reduced computational budget and no oracle-knowledge of request times the dynamic EMOA is capable of producing approximations which partially dominate the results of the a-posteriori EMOA and dynamic integer linear programming strategies.}}, author = {{Bossek, Jakob and Grimme, Christian and Meisel, Stephan and Rudolph, Günter and Trautmann, Heike}}, booktitle = {{Evolutionary Multi-Criterion Optimization (EMO)}}, editor = {{Deb, Kalyanmoy and Goodman, Erik and Coello Coello, Carlos A. and Klamroth, Kathrin and Miettinen, Kaisa and Mostaghim, Sanaz and Reed, Patrick}}, isbn = {{978-3-030-12598-1}}, keywords = {{Combinatorial optimization, Dynamic optimization, Metaheuristics, Multi-objective optimization, Vehicle routing}}, pages = {{516–528}}, publisher = {{Springer International Publishing}}, title = {{{Bi-Objective Orienteering: Towards a Dynamic Multi-objective Evolutionary Algorithm}}}, doi = {{10.1007/978-3-030-12598-1_41}}, year = {{2019}}, } @inproceedings{48842, abstract = {{Evolutionary algorithms have successfully been applied to evolve problem instances that exhibit a significant difference in performance for a given algorithm or a pair of algorithms inter alia for the Traveling Salesperson Problem (TSP). Creating a large variety of instances is crucial for successful applications in the blooming field of algorithm selection. In this paper, we introduce new and creative mutation operators for evolving instances of the TSP. We show that adopting those operators in an evolutionary algorithm allows for the generation of benchmark sets with highly desirable properties: (1) novelty by clear visual distinction to established benchmark sets in the field, (2) visual and quantitative diversity in the space of TSP problem characteristics, and (3) significant performance differences with respect to the restart versions of heuristic state-of-the-art TSP solvers EAX and LKH. The important aspect of diversity is addressed and achieved solely by the proposed mutation operators and not enforced by explicit diversity preservation.}}, author = {{Bossek, Jakob and Kerschke, Pascal and Neumann, Aneta and Wagner, Markus and Neumann, Frank and Trautmann, Heike}}, booktitle = {{Proceedings of the 15th ACM/SIGEVO Conference on Foundations of Genetic Algorithms}}, isbn = {{978-1-4503-6254-2}}, keywords = {{benchmarking, instance features, optimization, problem generation, traveling salesperson problem}}, pages = {{58–71}}, publisher = {{Association for Computing Machinery}}, title = {{{Evolving Diverse TSP Instances by Means of Novel and Creative Mutation Operators}}}, doi = {{10.1145/3299904.3340307}}, year = {{2019}}, } @inproceedings{48843, abstract = {{We contribute to the theoretical understanding of randomized search heuristics for dynamic problems. We consider the classical graph coloring problem and investigate the dynamic setting where edges are added to the current graph. We then analyze the expected time for randomized search heuristics to recompute high quality solutions. This includes the (1+1) EA and RLS in a setting where the number of colors is bounded and we are minimizing the number of conflicts as well as iterated local search algorithms that use an unbounded color palette and aim to use the smallest colors and - as a consequence - the smallest number of colors. We identify classes of bipartite graphs where reoptimization is as hard as or even harder than optimization from scratch, i. e. starting with a random initialization. Even adding a single edge can lead to hard symmetry problems. However, graph classes that are hard for one algorithm turn out to be easy for others. In most cases our bounds show that reoptimization is faster than optimizing from scratch. Furthermore, we show how to speed up computations by using problem specific operators concentrating on parts of the graph where changes have occurred.}}, author = {{Bossek, Jakob and Neumann, Frank and Peng, Pan and Sudholt, Dirk}}, booktitle = {{Proceedings of the Genetic and Evolutionary Computation Conference}}, isbn = {{978-1-4503-6111-8}}, keywords = {{dynamic optimization, evolutionary algorithms, running time analysis, theory}}, pages = {{1443–1451}}, publisher = {{Association for Computing Machinery}}, title = {{{Runtime Analysis of Randomized Search Heuristics for Dynamic Graph Coloring}}}, doi = {{10.1145/3321707.3321792}}, year = {{2019}}, }