@article{64916,
abstract = {{The joining of dissimilar materials, such as steel and aluminum, entails significant challenges during thermal curing processes due to differing coefficients of thermal expansion. This study addresses the formation of “viscous fingering” instabilities in structural adhesive joints, which are induced by thermally driven relative displacements during the liquid phase of the adhesive. Using a component-like specimen “bridge specimen,” the dependency of this phenomenon on process temperature and structural stiffness (rivet distance) was characterized. Experimental results reveal that while the relative displacement scales cubically with the free buckling length, the resulting adhesive area reduction follows an exponential trend, leading to a loss of effective bond area of up to 79%, which significantly compromises the joint strength in automotive applications. To predict these process-induced defects, a thermo-chemo-viscoelastic-viscoplastic adhesive model implemented in LS-DYNA was applied. The model combines curing kinetics, viscoelastic relaxation, and pressure-dependent plasticity and features a geometric damage parameter (D) that captures the adhesive area reduction caused by viscous fingering as an exponential function of the accumulated normal strain in the liquid phase. This damage parameter, calibrated on base-specimen level, was transferred to the component geometry. The simulation demonstrated high predictive accuracy with a maximum deviation of the adhesive area reduction of 3.1% compared to experimental data. This validates the model’s capability to predict manufacturing-induced damage in complex hybrid structures solely based on thermal boundary conditions.}},
author = {{Al Trjman, Mohamad and Beule, Felix and Teutenberg, Dominik and Meschut, Gerson and Riese, Julia}},
issn = {{0021-8464}},
journal = {{The Journal of Adhesion}},
keywords = {{Adhesive area reduction, CED coating process, delta alpha problem, epoxy structural adhesive, influence of manufacture, multi-material design, numerical simulation (FEM), relative displacements, viscous fingering (saffman-taylor-instability).}},
pages = {{1--24}},
publisher = {{Informa UK Limited}},
title = {{{Experimental characterization and numerical analysis of the influence of the CED coating process on viscous fingering formation in hybrid-jointed mixed structures}}},
doi = {{10.1080/00218464.2026.2644394}},
year = {{2026}},
}
@article{65495,
abstract = {{This paper presents a holistic framework for the transition from diesel to electric bus networks,
crucial for meeting EU regulations targeting 100% zero-emission urban buses by
2035. We employ a two-phase solution framework: in phase 1, we solve the Charging Location
and Electric Vehicle Scheduling Problem to generate vehicle schedules that are feasible
for electric operation; in phase 2, these schedules serve as input to a multi-period transition
planning model that minimizes the total cost of ownership while determining fleet
replacement and charging infrastructure deployment. Our experiments show that schedules
obtained from solving the integrated charging location and vehicle scheduling problem
significantly outperform traditional methods, resulting in lower total cost of ownership. Additionally,
transition plans reduce local emissions by up to 85% compared to a diesel-only
scenario. We find that vehicle rotations with long distances and sufficient idle time are
prioritized for electrification, enabling earlier emission reductions and cost savings. This
highlights the importance of adopting vehicle scheduling tailored for electric buses, rather
than relying on legacy diesel schedules.}},
author = {{Stumpe, Miriam and Rößler-von Saß, David and Natalia, Kliewer and Schryen, Guido}},
journal = {{Transportation Research Interdisciplinary Perspectives}},
keywords = {{electric bus, multi-period planning, electric vehicle scheduling, charging infrastructure, fleet replacement}},
title = {{{Impact of Vehicle Scheduling and Strategic Transition Planning on Zero-Emission Bus Systems}}},
year = {{2026}},
}
@article{65757,
abstract = {{Electrifying the heating sector is essential for achieving global climate targets like the Paris Agreement’s 1.5 °C goal. In Germany, where 80 % of household energy goes to space heating and hot water, shifting to low-carbon solutions is crucial. Fossil-fuelled district heating networks can incorporate renewable energy via heat pumps, improving efficiency. Although heat pump design typically favours minimal temperature lifts, higher lifts can be economically viable with low electricity prices and abundant renewables. Adding thermal energy storage boosts operational flexibility. This study explores a flexible heat pump operation incorporating part load behaviour with a thermal energy storage in a German city’s district heating system to minimise costs and carbon dioxide emissions. Using a mixed-integer linear programming model, it examines the impact of temperature adjustments and storage on system efficiency. The results show that the integration of a heat pump in a district heating system reduces operating costs. Compared to a supply without a heat pump, with levelised cost of heating of 9.98 cent/kWhth and a fixed operating mode with costs between 9.96 cent/kWhth and 11.49 cent/kWhth, the flexible use results in lowest costs of 9.85 cent/kWhth, while also reducing overall CO2 emissions. Using a full factorial sensitivity analysis, the levelised cost of heating ranged between 9.15 cent/kWhth in the best case and 10.37 cent/kWhth in the worst case for the selected configuration.}},
author = {{Rahlf, Henning Christoph and Divkovic, Denis and Knorr, Lukas and Schlosser, Florian and Meschede, Henning}},
issn = {{0196-8904}},
journal = {{Energy Conversion and Management}},
keywords = {{Heat transition, Optimisation, Temperature flexibility, Decarbonisation, Multi energy}},
publisher = {{Elsevier BV}},
title = {{{Flexible operation strategies for heat pumps in district heating systems using dynamic electricity prices}}},
doi = {{10.1016/j.enconman.2026.121714}},
volume = {{364}},
year = {{2026}},
}
@article{65857,
abstract = {{This paper presents a holistic framework for the transition from diesel to electric bus networks, crucial for meeting EU regulations targeting 100% zero-emission urban buses by 2035. We employ a two-phase solution framework: in phase 1, we solve the Charging Location and Electric Vehicle Scheduling Problem to generate vehicle schedules that are feasible for electric operation; in phase 2, these schedules serve as input to a multi-period transition planning model that minimizes the total cost of ownership while determining fleet replacement and charging infrastructure deployment. Our experiments show that schedules obtained from solving the integrated charging location and vehicle scheduling problem significantly outperform traditional methods, resulting in lower total cost of ownership. Additionally, transition plans reduce local emissions by up to 85% compared to a diesel-only scenario. We find that vehicle rotations with long distances and sufficient idle time are prioritized for electrification, enabling earlier emission reductions and cost savings. This highlights the importance of adopting vehicle scheduling tailored for electric buses, rather than relying on legacy diesel schedules.}},
author = {{Stumpe, Miriam and Rößler-von Saß, David and Kliewer, Natalia and Schryen, Guido}},
issn = {{2590-1982}},
journal = {{Transportation Research Interdisciplinary Perspectives}},
keywords = {{Electric bus, Multi-period planning, Electric vehicle scheduling, Charging infrastructure, Fleet replacement}},
publisher = {{Elsevier BV}},
title = {{{Impact of vehicle scheduling and strategic transition planning on zero-emission bus systems}}},
doi = {{10.1016/j.trip.2026.102008}},
volume = {{38}},
year = {{2026}},
}
@article{66001,
abstract = {{Expanding renewable energy sources is essential for a sustainable energy supply but challenges grid stability, as the volatility of solar and wind causes periods of over- and undersupply. Private households are central to this transition, combining dynamic consumption with decentralised generation. This paper presents a multi-agent microgrid simulation built on the Mesa framework, focusing on the heterogeneous objectives and technological capabilities of residential participants. Households are modelled as autonomous agents with individual strategies, while a dedicated “grid agent” represents the distribution system operator and regulates the microgrid in a grid-supportive manner. The emission factor serves as the key indicator for grid-friendly behaviour. Results show that in summer, unmanaged PV feed-in from heterogeneous households causes substantial grid stress and balancing effort for the distribution system operator. Dynamic electricity prices can incentivise grid-friendly dispatch, but their effectiveness depends on the correlation between price signals and renewable availability and cannot guarantee grid-supportive behaviour alone. The grid agent reliably improves the grid-supportive coefficient, yet its operating strategy, for instance additional peak-reduction objectives, can interfere with price-based incentives. Effective demand-side management therefore requires careful analysis of stakeholder interactions. Building on this insight, thepaper provides a basic framework for the design, implementation, and assessment of both integrated and individual energy management strategies within a microgrid environment. By simulating the dynamic interactions among system participants and strategies, it enables comprehensive evaluation of their collective impact on the grid, supporting the development of robust solutions for future electricity networks.}},
author = {{Henne, Kevin and Rahlf, Henning Christoph and Naumann, Marius and Meschede, Henning}},
issn = {{2590-1745}},
journal = {{Energy Conversion and Management: X}},
keywords = {{Microgrid, Decentralised Energy Systems, Multi-Agent System, Residential Demand-Side Management, Optimisation}},
location = {{Dubrovnik}},
pages = {{102030}},
publisher = {{Elsevier}},
title = {{{Towards Stakeholder-Aware Demand-Side management assessment in heterogeneous residential Microgrids: A Multi-Agent approach}}},
doi = {{https://doi.org/10.1016/j.ecmx.2026.102030}},
volume = {{31}},
year = {{2026}},
}
@inproceedings{66288,
abstract = {{ABSTRACT:
Engineers simulate system behavior to support decisions in product engineering. Leveraging such engineering simulation data in strategic product planning can support idea generation and early evaluation of design alternatives and limitations. However, limited resources and expertise hinder broader uptake in strategic product planning. This paper investigates simulator integration into automated workflows and key processing components to enable simulation without in-depth expertise. This approach improves strategic product planning by creating data-based decision support.}},
author = {{Gräßler, Iris and Döhner, Niklas}},
booktitle = {{Proceedings of the Design Society}},
issn = {{2732-527X}},
keywords = {{simulation-based design, design tools, multi-/cross-/trans-disciplinary approaches, simulation data reuse}},
pages = {{357--366}},
publisher = {{Cambridge University Press (CUP)}},
title = {{{Leveraging extreme-scale simulation data: a workflow framework for multidisciplinary simulator integration}}},
doi = {{10.1017/pds.2026.10394}},
volume = {{6}},
year = {{2026}},
}
@article{55400,
abstract = {{This study contributes to the evolving field of robot learning in interaction
with humans, examining the impact of diverse input modalities on learning
outcomes. It introduces the concept of "meta-modalities" which encapsulate
additional forms of feedback beyond the traditional preference and scalar
feedback mechanisms. Unlike prior research that focused on individual
meta-modalities, this work evaluates their combined effect on learning
outcomes. Through a study with human participants, we explore user preferences
for these modalities and their impact on robot learning performance. Our
findings reveal that while individual modalities are perceived differently,
their combination significantly improves learning behavior and usability. This
research not only provides valuable insights into the optimization of
human-robot interactive task learning but also opens new avenues for enhancing
the interactive freedom and scaffolding capabilities provided to users in such
settings.}},
author = {{Beierling, Helen and Beierling, Robin and Vollmer, Anna-Lisa}},
journal = {{Frontiers in Robotics and AI}},
keywords = {{human-robot interaction, human-in-the-loop learning, reinforcement learning, interactive robot learning, multi-modal feedback, learning from demonstration, preference-based learning, scaffolding in robot learning}},
publisher = {{Frontiers }},
title = {{{The power of combined modalities in interactive robot learning}}},
volume = {{12}},
year = {{2025}},
}
@inproceedings{62066,
abstract = {{In the context of high-performance computing (HPC) for distributed workloads, individual field-programmable gate arrays (FPGAs) need efficient ways to exchange data, which requires network infrastructure and software abstractions. Dedicated multi-FPGA clusters provide inter-FPGA networks for direct device to device communication. The oneAPI high-level synthesis toolchain offers I/O pipes to allow user kernels to interact with the networking ports of the FPGA board. In this work, we evaluate using oneAPI I/O pipes for direct FPGA-to-FPGA communication by scaling a SYCL implementation of a Jacobi solver on up to 25 FPGAs in the Noctua 2 cluster. We see good results in weak and strong scaling experiments.}},
author = {{Alt, Christoph and Plessl, Christian and Kenter, Tobias}},
booktitle = {{Proceedings of the 13th International Workshop on OpenCL and SYCL}},
isbn = {{9798400713606}},
keywords = {{Multi-FPGA, High-level Synthesis, oneAPI, FPGA}},
publisher = {{Association for Computing Machinery}},
title = {{{Evaluating oneAPI I/O Pipes in a Case Study of Scaling a SYCL Jacobi Solver to multiple FPGAs}}},
doi = {{10.1145/3731125.3731131}},
year = {{2025}},
}
@article{63053,
author = {{Hernández, Carlos and Rodriguez-Fernandez, Angel E. and Schäpermeier, Lennart and Cuate, Oliver and Trautmann, Heike and Schütze, Oliver}},
journal = {{IEEE Transactions on Evolutionary Computation}},
keywords = {{Optimization, Evolutionary computation, Hands, Proposals, Convergence, Computational efficiency, Artificial intelligence, Accuracy, Approximation algorithms, Aerospace electronics, Multi-objective optimization, evolutionary algorithms, nearly optimal solutions, multimodal optimization, archiving, continuation}},
pages = {{1--1}},
title = {{{An Evolutionary Approach for the Computation of ∈-Locally Optimal Solutions for Multi-Objective Multimodal Optimization}}},
doi = {{10.1109/TEVC.2025.3637276}},
year = {{2025}},
}
@article{56221,
author = {{Rodriguez-Fernandez, Angel E. and Schäpermeier, Lennart and Hernández, Carlos and Kerschke, Pascal and Trautmann, Heike and Schütze, Oliver}},
journal = {{IEEE Transactions on Evolutionary Computation}},
keywords = {{Optimization, Evolutionary computation, Approximation algorithms, Benchmark testing, Vectors, Surveys, Pareto optimization, multi-objective optimization, evolutionary computation, multimodal optimization, local solutions}},
pages = {{1--1}},
title = {{{Finding ϵ-Locally Optimal Solutions for Multi-Objective Multimodal Optimization}}},
doi = {{10.1109/TEVC.2024.3458855}},
year = {{2024}},
}
@inproceedings{56636,
abstract = {{Abstract. Business reputation ecosystems are a widely untapped research field. In these ecosystems, agents can selectively exchange (monetary) ratings to in-form about the experienced quality in a market. We build a model for conducting a multi-agent simulation that can be used to simulate and evaluate business rep-utation ecosystems as a new system class. We explore the factual occurring vol-untary payment to create positive (pay) or negative ratings (no pay), selling rat-ings selectively to alleviate information asymmetry, and the workings of counter-ratings to prevent buyers' dishonest ratings. Thereby, we analyze, among others, agent profitability, the occurrence of dishonest ratings, and reputation bias and sensitivity. The results provide simulation-based empirical evidence that the con-cept of monetary reputation systems provides necessary incentives for participa-tion, and high-quality sellers and honest buyers benefit from such a system. The results indicate that counter-ratings prompt buyers}},
author = {{Ibrahimli, Ulvi and Hemmrich, Simon and Zauke, Simon and Winkelmann, Axel}},
booktitle = {{19. Internationale Tagung Wirtschaftsinformatik (WI24)}},
keywords = {{Reputation System, Payment as Rating, Multi-Agent Simulation, Lemon Markets}},
location = {{Würzburg}},
title = {{{Overcoming Lemon Markets with Business Reputation Ecosystem – A Multi-agent Simulation on Monetary Ratings}}},
year = {{2024}},
}
@inproceedings{45812,
author = {{Özcan, Leon and Fichtler, Timm and Kasten, Benjamin and Koldewey, Christian and Dumitrescu, Roman}},
keywords = {{Digital Platform, Platform Strategy, Strategic Management, Platform Life Cycle, Interview Study, Business Model, Business-to-Business, Two-sided Market, Multi-sided Market}},
location = {{Ljubljana}},
title = {{{Interview Study on Strategy Options for Platform Operation in B2B Markets}}},
year = {{2023}},
}
@inproceedings{33991,
abstract = {{In the course of digitalization, digital platforms are unleashing their full disruptive potential and are already dominating the first industries (e.g., hotel industry). As a result of this success, more and more companies want to build their own platforms and participate in the success. However, building and operating a digital platform involves multiple challenges and most of such ambitions fail. Since most digital platforms fail, strategic leadership of digital platforms must consider both success factors and reasons for platform failure. For this purpose, we conducted a systematic literature analysis and identified 24 success as well as failure factors in 9 dimensions. From a scientific perspective, the article provides a structured analysis of success and failure factors of digital platforms, which previously did not exist in literature. Practitioners can use the resulting knowledge base to successfully manage platform activities and avoid pitfalls.}},
author = {{Özcan, Leon and Koldewey, Christian and Duparc, Estelle and van der Valk, Hendrik and Otto, Boris and Dumitrescu, Roman}},
keywords = {{Digital Platform, Multi-sided Market, Two-sided Market, Success Factor, Failure Factor}},
location = {{Minneapolis}},
title = {{{Why do Digital Platforms succeed or fail? - A Literature Review on Success and Failure Factors}}},
year = {{2022}},
}
@inbook{34209,
abstract = {{Predicting the durability of components subjected to mechanical load under environmental conditions leading to corrosion is one of the most challenging tasks in mechanical engineering. The demand for precise predictions increases with the desire of lightweight design in transportation due to environmental protection. Corrosion with its manifold of mechanisms often occurs together with the production of hydrogen by electrochemical reactions. Hydrogen embrittlement is one of the most feared damage mechanisms for metal constructions often leading to early and unexpected failure. Until now, predictions are mostly based on costly experiments. Hence, a rational predictive model based on the fundamentals of electrochemistry and damage mechanics has to be developed in order to reduce the costs. In this work, a first model approach based on classical continuum damage mechanics is presented to couple both, the damage induced by the mechanical stress and the hydrogen embrittlement. An elaborated two-scale model based on the selfconsistent theory is applied to describe the mechanical damage due to fatigue. The electrochemical kinetics are elucidated through the Langmuir adsorption isotherm and the diffusion equation to consider the impact of hydrogen embrittlement on the fatigue. The modeling of the mechanism of hydrogen embrittlement defines the progress of damage accumulation due to the electrochemistry. The durability results like the S-N diagram show the influence of hydrogen embrittlement by varying, e.g. the fatigue frequency or the stress ratio.}},
author = {{Shi, Yuhao and Harzheim, Sven and Hofmann, Martin and Wallmersperger, Thomas}},
booktitle = {{Material Modeling and Structural Mechanics}},
isbn = {{9783030976743}},
issn = {{1869-8433}},
keywords = {{Hydrogen embrittlement, Fatigue, Continuum damage mechanics, Numerical simulation, Multi-field problem}},
publisher = {{Springer International Publishing}},
title = {{{A Damage Model for Corrosion Fatigue Due to Hydrogen Embrittlement}}},
doi = {{10.1007/978-3-030-97675-0_9}},
year = {{2022}},
}
@inproceedings{48882,
abstract = {{In multimodal multi-objective optimization (MMMOO), the focus is not solely on convergence in objective space, but rather also on explicitly ensuring diversity in decision space. We illustrate why commonly used diversity measures are not entirely appropriate for this task and propose a sophisticated basin-based evaluation (BBE) method. Also, BBE variants are developed, capturing the anytime behavior of algorithms. The set of BBE measures is tested by means of an algorithm configuration study. We show that these new measures also transfer properties of the well-established hypervolume (HV) indicator to the domain of MMMOO, thus also accounting for objective space convergence. Moreover, we advance MMMOO research by providing insights into the multimodal performance of the considered algorithms. Specifically, algorithms exploiting local structures are shown to outperform classical evolutionary multi-objective optimizers regarding the BBE variants and respective trade-off with HV.}},
author = {{Heins, Jonathan and Rook, Jeroen and Schäpermeier, Lennart and Kerschke, Pascal and Bossek, Jakob and Trautmann, Heike}},
booktitle = {{Parallel Problem Solving from Nature (PPSN XVII)}},
editor = {{Rudolph, Günter and Kononova, Anna V. and Aguirre, Hernán and Kerschke, Pascal and Ochoa, Gabriela and Tusar, Tea}},
isbn = {{978-3-031-14714-2}},
keywords = {{Anytime behavior, Benchmarking, Continuous optimization, Multi-objective optimization, Multimodality, Performance metric}},
pages = {{192–206}},
publisher = {{Springer International Publishing}},
title = {{{BBE: Basin-Based Evaluation of Multimodal Multi-objective Optimization Problems}}},
doi = {{10.1007/978-3-031-14714-2_14}},
year = {{2022}},
}
@inproceedings{48896,
abstract = {{Hardness of Multi-Objective (MO) continuous optimization problems results from an interplay of various problem characteristics, e. g. the degree of multi-modality. We present a benchmark study of classical and diversity focused optimizers on multi-modal MO problems based on automated algorithm configuration. We show the large effect of the latter and investigate the trade-off between convergence in objective space and diversity in decision space.}},
author = {{Rook, Jeroen and Trautmann, Heike and Bossek, Jakob and Grimme, Christian}},
booktitle = {{Proceedings of the Genetic and Evolutionary Computation Conference Companion}},
isbn = {{978-1-4503-9268-6}},
keywords = {{configuration, multi-modality, multi-objective optimization}},
pages = {{356–359}},
publisher = {{Association for Computing Machinery}},
title = {{{On the Potential of Automated Algorithm Configuration on Multi-Modal Multi-Objective Optimization Problems}}},
doi = {{10.1145/3520304.3528998}},
year = {{2022}},
}
@inproceedings{33997,
abstract = {{Digital platforms have already led to disruptions in multiple B2C markets and are becoming increasingly dominant in B2B markets. As a result, more and more companies are trying to participate in the platform economy. However, the successful development and operation of a digital platform is associated with significant challenges, which leads to 85% of all platforms failing. A core challenge is the dynamic nature of the platform economy, with varying strategic objectives at different stages in the platform lifecycle. Platform operators must continuously monitor platform progress and adjust their strategy.
Utilizing action research in the real-world platform project AI Marketplace, we developed a lifecycle-oriented performance management approach for digital platforms in B2B markets. It enables platform operators to reflect on their position in the platform lifecycle, derive relevant strategic objectives, and monitor them with suitable key performance indicators. Hence, allowing them to secure the long-term success of their platform business.}},
author = {{Özcan, Leon and Kirchberg, Lisa Irene and Koldewey, Christian and Dumitrescu, Roman}},
booktitle = {{The Role of Innovation: Past, Present, Future}},
editor = {{Bitran, Iain and Bitetti, Leandro and Conn, Steffen and Fishburn, Jessica and Huizingh, Eelko and Torkkeli, Marko and Yang, Jialei}},
keywords = {{Digital Platform, Two-Sided Market, Multi-Sided Market, Platform Lifecycle, Platform Monitoring, Performance Management}},
location = {{Athens}},
title = {{{Performance Management Approach for Digital Platforms in B2B Markets}}},
year = {{2022}},
}
@inproceedings{31066,
abstract = {{While trade-offs between modeling effort and model accuracy remain a major concern with system identification, resorting to data-driven methods often leads to a complete disregard for physical plausibility. To address this issue, we propose a physics-guided hybrid approach for modeling non-autonomous systems under control. Starting from a traditional physics-based model, this is extended by a recurrent neural network and trained using a sophisticated multi-objective strategy yielding physically plausible models. While purely data-driven methods fail to produce satisfying results, experiments conducted on real data reveal substantial accuracy improvements by our approach compared to a physics-based model. }},
author = {{Schön, Oliver and Götte, Ricarda-Samantha and Timmermann, Julia}},
booktitle = {{14th IFAC Workshop on Adaptive and Learning Control Systems (ALCOS 2022)}},
keywords = {{neural networks, physics-guided, data-driven, multi-objective optimization, system identification, machine learning, dynamical systems}},
location = {{Casablanca, Morocco}},
number = {{12}},
pages = {{19--24}},
title = {{{Multi-Objective Physics-Guided Recurrent Neural Networks for Identifying Non-Autonomous Dynamical Systems}}},
doi = {{https://doi.org/10.1016/j.ifacol.2022.07.282}},
volume = {{55}},
year = {{2022}},
}
@article{21004,
abstract = {{Automated machine learning (AutoML) supports the algorithmic construction and data-specific customization of machine learning pipelines, including the selection, combination, and parametrization of machine learning algorithms as main constituents. Generally speaking, AutoML approaches comprise two major components: a search space model and an optimizer for traversing the space. Recent approaches have shown impressive results in the realm of supervised learning, most notably (single-label) classification (SLC). Moreover, first attempts at extending these approaches towards multi-label classification (MLC) have been made. While the space of candidate pipelines is already huge in SLC, the complexity of the search space is raised to an even higher power in MLC. One may wonder, therefore, whether and to what extent optimizers established for SLC can scale to this increased complexity, and how they compare to each other. This paper makes the following contributions: First, we survey existing approaches to AutoML for MLC. Second, we augment these approaches with optimizers not previously tried for MLC. Third, we propose a benchmarking framework that supports a fair and systematic comparison. Fourth, we conduct an extensive experimental study, evaluating the methods on a suite of MLC problems. We find a grammar-based best-first search to compare favorably to other optimizers.}},
author = {{Wever, Marcel Dominik and Tornede, Alexander and Mohr, Felix and Hüllermeier, Eyke}},
issn = {{0162-8828}},
journal = {{IEEE Transactions on Pattern Analysis and Machine Intelligence}},
keywords = {{Automated Machine Learning, Multi Label Classification, Hierarchical Planning, Bayesian Optimization}},
pages = {{1--1}},
title = {{{AutoML for Multi-Label Classification: Overview and Empirical Evaluation}}},
doi = {{10.1109/tpami.2021.3051276}},
year = {{2021}},
}
@article{21808,
abstract = {{Modern services consist of interconnected components,e.g., microservices in a service mesh or machine learning functions in a pipeline. These services can scale and run across multiple network nodes on demand. To process incoming traffic, service components have to be instantiated and traffic assigned to these instances, taking capacities, changing demands, and Quality of Service (QoS) requirements 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 DeepCoord, 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 on 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, DeepCoord significantly improves flow throughput (up to 76%) and overall network utility (more than 2x) on realworld network topologies and traffic traces. It also supports optimizing multiple, possibly competing objectives, learns to respect QoS requirements, generalizes to scenarios with unseen, stochastic traffic, and scales to large real-world networks. For reproducibility and reuse, our code is publicly available.}},
author = {{Schneider, Stefan Balthasar and Khalili, Ramin and Manzoor, Adnan and Qarawlus, Haydar and Schellenberg, Rafael and Karl, Holger and Hecker, Artur}},
journal = {{Transactions on Network and Service Management}},
keywords = {{network management, service management, coordination, reinforcement learning, self-learning, self-adaptation, multi-objective}},
publisher = {{IEEE}},
title = {{{Self-Learning Multi-Objective Service Coordination Using Deep Reinforcement Learning}}},
doi = {{10.1109/TNSM.2021.3076503}},
year = {{2021}},
}