@unpublished{33150,
  abstract     = {{In this article, we build on previous work to present an optimization algorithm for nonlinearly constrained multi-objective optimization problems. The algorithm combines a surrogate-assisted derivative-free trust-region approach with the filter method known from single-objective optimization. Instead of the true objective and constraint functions, so-called fully linear models are employed and we show how to deal with the gradient inexactness in the composite step setting, adapted from single-objective optimization as well. Under standard assumptions, we prove convergence of a subset of iterates to a quasi-stationary point and if constraint qualifications hold, then the limit point is also a KKT-point of the multi-objective problem.}},
  author       = {{Berkemeier, Manuel Bastian and Peitz, Sebastian}},
  booktitle    = {{arXiv:2208.12094}},
  title        = {{{Multi-Objective Trust-Region Filter Method for Nonlinear Constraints using Inexact Gradients}}},
  year         = {{2022}},
}

@inproceedings{33230,
  author       = {{Daymude, Joshua J. and Richa, Andréa W. and Scheideler, Christian}},
  booktitle    = {{1st Symposium on Algorithmic Foundations of Dynamic Networks, SAND 2022, March 28-30, 2022, Virtual Conference}},
  editor       = {{Aspnes, James and Michail, Othon}},
  pages        = {{12:1–12:19}},
  publisher    = {{Schloss Dagstuhl - Leibniz-Zentrum für Informatik}},
  title        = {{{Local Mutual Exclusion for Dynamic, Anonymous, Bounded Memory Message Passing Systems}}},
  doi          = {{10.4230/LIPIcs.SAND.2022.12}},
  volume       = {{221}},
  year         = {{2022}},
}

@inproceedings{33240,
  author       = {{Götte, Thorsten and Scheideler, Christian}},
  booktitle    = {{SPAA ’22: 34th ACM Symposium on Parallelism in Algorithms and Architectures, Philadelphia, PA, USA, July 11 - 14, 2022}},
  editor       = {{Agrawal, Kunal and Lee, I-Ting Angelina}},
  pages        = {{99–101}},
  publisher    = {{ACM}},
  title        = {{{Brief Announcement: The (Limited) Power of Multiple Identities: Asynchronous Byzantine Reliable Broadcast with Improved Resilience through Collusion}}},
  doi          = {{10.1145/3490148.3538556}},
  year         = {{2022}},
}

@inbook{30941,
  abstract     = {{Decision support systems are crucial in helping decision makers to quickly identify optimal business decisions in increasingly volatile and complex business environments. However, the ideal DSS for one decision maker may not optimally address the requirements for decision support of another decision maker. This is due to differences between
decision makers in business goals, regulatory restrictions or availability of resources such as data. By using a suboptimal DSS, decision makers risk implementing suboptimal decision recommendations which endanger the success of their business. This presents DSS developers with the challenge to implement a customizable DSS which can be tailored to the individual requirements for decision support of a single decision maker. In order to address this challenge, we suggest a decision support ecosystem in which DSS developers, decision makers and other domain experts collaborate using a shared platform to provide and combine reusable decision support services into a tailored DSS. The contribution of our paper is twofold: First, we define the concept of a decision support ecosystem with respect to existing digital business ecosystems and discuss expected benefits and challenges. Second, we present a reference architecture for a shared platform supporting the realization of a decision support ecosystem. We demonstrate our contributions in the example application domain of regional energy distribution network planning.}},
  author       = {{Kirchhoff, Jonas and Weskamp, Christoph and Engels, Gregor}},
  booktitle    = {{Decision Support Systems XII: Decision Support Addressing Modern Industry, Business, and Societal Needs}},
  publisher    = {{Springer}},
  title        = {{{Decision Support Ecosystems: Definition and Platform Architecture}}},
  doi          = {{10.1007/978-3-031-06530-9_8}},
  volume       = {{447}},
  year         = {{2022}},
}

@inproceedings{33281,
  abstract     = {{Corporate decision makers have individual requirements for decision support influenced by business goals, regulatory restrictions or access to resources such as data. Ideally, decision makers could quickly create tailored decision support systems (DSS) themselves which optimally address their individual requirements for decision support. Although service-oriented architectures have been proposed for DSS customization, they are primarily targeting trained software developers and cannot immediately be adapted by decision makers or domain experts with little to no software development knowledge. In this paper, we therefore motivate an assisted process-based service composition approach which can be used by non-developers to create tailored DSS. For assistance during service composition, we contribute a meta-model for the formalization of both decision support requirements and functionality of decision support services. Models created according to the meta-model can be used to detect mismatches between a decision maker’s requirements for decision support and services selected in the service composition representing a DSS. Furthermore, the formalizations may even be used for automated service composition given a decision maker’s decision support requirements. We demonstrate the expressiveness of our meta-model in the domain of regional energy distribution network planning.}},
  author       = {{Kirchhoff, Jonas and Weskamp, Christoph and Engels, Gregor}},
  booktitle    = {{Human-Centered Software Engineering}},
  editor       = {{Bernhaupt, Regina and Ardito, Carmelo and Sauer, Stefan}},
  isbn         = {{978-3-031-14785-2}},
  pages        = {{150–162}},
  publisher    = {{Springer International Publishing}},
  title        = {{{Requirements-Based Composition of Tailored Decision Support Systems}}},
  doi          = {{10.1007/978-3-031-14785-2_10}},
  volume       = {{13482}},
  year         = {{2022}},
}

@inbook{29872,
  author       = {{Maack, Marten and Meyer auf der Heide, Friedhelm and Pukrop, Simon}},
  booktitle    = {{Approximation and Online Algorithms}},
  isbn         = {{9783030927011}},
  issn         = {{0302-9743}},
  publisher    = {{Springer International Publishing}},
  title        = {{{Server Cloud Scheduling}}},
  doi          = {{10.1007/978-3-030-92702-8_10}},
  year         = {{2022}},
}

@inproceedings{33503,
  author       = {{Gabriel, Stefan and Aring, Theresa and Hobscheidt, Daniela and Kühn, Arno and Dumitrescu, Roman}},
  booktitle    = {{Tagungsband des 68. Frühjahrskongress der Gesellschaft für Arbeitswissenschaft}},
  location     = {{Magdeburg 02.03. - 04.03.2022}},
  publisher    = {{GfA-Press}},
  title        = {{{Handlungsfelder für die KI-Einführung in der Arbeitswelt produzierender Unternehmen}}},
  year         = {{2022}},
}

@inproceedings{33505,
  author       = {{Anacker, Harald and Günther, Matthias and Wyrwich, Fabian and Dumitrescu, Roman}},
  booktitle    = {{17th Annual System of Systems Engineering Conference (SOSE)}},
  location     = {{Rochester, NY, USA}},
  pages        = {{178--183}},
  title        = {{{Pattern based engineering of System of Systems - a systematic literature review}}},
  doi          = {{10.1109/SOSE55472.2022.9812697}},
  year         = {{2022}},
}

@inproceedings{31188,
  author       = {{Anacker, Harald and Dumitrescu, Roman and Könemann, Ulf and Wilke, Daria}},
  booktitle    = {{ Proceedings of the 16th Annual IEEE International Systems Conference}},
  location     = {{Montreal, Canada}},
  title        = {{{Identification of stakeholder-specific Systems Engineering competencies for industry}}},
  year         = {{2022}},
}

@book{33516,
  author       = {{Fazal-Baqaie, Masud  and Linssen, Oliver and Volland, Alexander and Yigitbas, Enes and Engstler, Martin and Bertram, Martin and Kalenborn, Axel}},
  publisher    = {{Gesellschaft für Informatik e.V.}},
  title        = {{{Projektmanagement und Vorgehensmodelle 2022. Virtuelle Zusammenarbeit und verlorene Kulturen?}}},
  volume       = {{P 327}},
  year         = {{2022}},
}

@inproceedings{33553,
  author       = {{Pfeifer, Stefan and Akgül, Didem and Röbenack, Silke and Tihlarik, Amelie and Albert, Bruno and Anacker, Harald and Dumitrescu, Roman}},
  booktitle    = {{DS 118: Proceedings of NordDesign 2022}},
  editor       = {{Mortensen, N.H. and Hansen, C.T. and Deininger, M.}},
  isbn         = {{9781912254170}},
  location     = {{Copenhagen, Denmark}},
  publisher    = {{The Design Society}},
  title        = {{{Design Decisions in the Architecture Development of Advanced Systems: Towards traceable and sustainable Documentation and Communication}}},
  doi          = {{10.35199/norddesign2022}},
  year         = {{2022}},
}

@inproceedings{33552,
  author       = {{Disselkamp, Jan-Philipp and Seidenberg, Tobias and Anacker, Harald and Dumitrescu, Roman}},
  booktitle    = {{Proceedings of the IEEE}},
  location     = {{Nancy, France}},
  title        = {{{Design of an optimised value creation network for zero emission ferries}}},
  year         = {{2022}},
}

@inproceedings{33556,
  author       = {{Eckertz, Daniel and Anacker, Harald and Dumitrescu, Roman}},
  booktitle    = {{Proceedings of the 5th International Conference on Information and Computer Technologies (ICICT)}},
  location     = {{New York City, NY, United States}},
  publisher    = {{IEEE}},
  title        = {{{Knowledge-based Interactive Configuration Tool for Industrial Augmented Reality Systems}}},
  doi          = {{10.1109/icict55905.2022.00021}},
  year         = {{2022}},
}

@inproceedings{33554,
  author       = {{Merkelbach, Silke and von Enzberg, Sebastian and Kuhn, Arno and Dumitrescu, Roman}},
  booktitle    = {{Proceedings of the IEEE}},
  publisher    = {{IEEE}},
  title        = {{{Towards a Process Model to Enable Domain Experts to Become Citizen Data Scientists for Industrial Applications}}},
  doi          = {{10.1109/icps51978.2022.9816871}},
  year         = {{2022}},
}

@inproceedings{33557,
  author       = {{Eckertz, Daniel and Anacker, Harald and Dumitrescu, Roman}},
  booktitle    = {{Proceedings of NordDesign 2022}},
  publisher    = {{The Design Society}},
  title        = {{{Systematics for the individual assessment of augmented reality potentials to support product validation}}},
  doi          = {{10.35199/norddesign2022.12}},
  year         = {{2022}},
}

@inproceedings{33558,
  author       = {{Wilke, Daria and Pfeifer, Stefan and Heitmann, Rebecca  and Anacker, Harald  and Dumitrescu, Roman and Franke, Volker }},
  booktitle    = {{Proceedings of the IEEE ISSE }},
  location     = {{Wien, Österreich}},
  title        = {{{Implementation of Systems Engineering: A maturity-based approach}}},
  year         = {{2022}},
}

@techreport{33702,
  abstract     = {{<jats:p>Im Rahmen dieser Studie wird der Status Quo des KI-Einsatzes in der industriellen Arbeitswelt in der Region OstWestfalenLippe erfasst und beschrieben. Dadurch wird eine Grundlage geschaffen, um eine zielführende Unterstützung der Gestaltung von durch Künstliche Intelligenz (KI) gestützter Arbeitsprozesse in Unternehmen zu ermöglichen, indem beispielsweise bedarfsbezogene Maßnahmen entwickelt und durchgeführt sowie weiterer Forschungsbedarf aufgezeigt wird.  Die Befragung wurde im Jahr 2021 von dem Kompetenzzentrum Arbeitswelt.Plus sowie dem Spitzencluster it’s OWL initiiert. Dabei sind drei Zielgruppen – Unternehmensleitung, Personalabteilung (HR) sowie Arbeitnehmer*innen – adressiert worden. Insgesamt nahmen 317 Personen aus 89 verschiedenen Unternehmen bzw. Organisationen an der Befragung teil – zu 38 % Unternehmer*innen, zu 13 % Personaler*innen und zu 49 % Arbeitnehmer*innen. Die meisten der Teilnehmenden stammten aus der Elektroindustrie, dem Maschinenbau sowie dem Informations- und Kommunikationstechnologie (IKT)-Sektor.  Die Befragungsergebnisse zeigen, dass sich die meisten Unternehmen in der Anfangsphase der KI-Nutzung befinden. Zwischen einzelnen Unternehmensbereichen und verschiedenen Branchen zeigen sich gewisse Unterschiede in der Nutzungsphase. Die Befragten stehen aktuell vor der Nutzung von vor allem teilautonomen KI-Systemen, die ausführende und analytische menschliche Tätigkeitenbeispielsweise durch Informationsbereitstellungen unterstützen. Wesentliche Ziele der KI-Nutzung sind die Effizienzsteigerung, Qualitätsverbesserung, Entscheidungsoptimierung sowie Unterstützung der Arbeitnehmer*innen. Allerdings werden in allen Unternehmen die fehlende Expertise sowie insgesamt die Komplexität des Themenfelds als Hinderungsgründe identifiziert.  In allen Unternehmen und allen Unternehmensbereichen werden hohe Auswirkungen durch KI erwartet. Auf die Arbeitsgestaltung werden insgesamt eher positive Auswirkungen erwartet. Die Befragten schätzen die Bedeutung von KI, ihre Aufgeschlossenheit sowie ihr Vertrauen gegenüber KI als insgesamt hoch ein, ihr Verständnis von KI dagegen eher als gering. Tendenziell zeigt sich eine große Diskrepanz zwischen Selbst- und Fremdbild mit einer teils deutlich negativeren Wahrnehmung anderer. Die Befragten erwarten außerdem steigende Kompetenzanforderungen sowie einen hohen Weiterbildungsbedarf, insbesondere bezüglich des grundlegenden Verständnisses über KI. In den wenigsten Unternehmen existiert jedoch ein gezieltes Weiterbildungsangebot.  Die Erkenntnisse aus der Befragung fließen im Rahmen des Kompetenzzentrums Arbeitswelt.Plus in die gezielte Gestaltung und Einführung KI-gestützter Arbeitsformen sowie bedarfsgerechter Unterstützungsangebote ein. Die hohe Komplexität der KI-Einführung sowie die sowohl technischen als auch mitarbeiterbezogenen Herausforderungen verdeutlichen den Bedarf für eine soziotechnische Perspektive und ein systematisches Vorgehen bei der Gestaltung dieses vielschichtigen Themenfelds.</jats:p>}},
  author       = {{Papenkordt, Jörg and Gabriel, Stefan and Thommes, Kirsten and Dumitrescu, Roman}},
  publisher    = {{Kompetenzzentrum Arbeitswelt.Plus}},
  title        = {{{Künstliche Intelligenz in der industriellen Arbeitswelt - Studie zum Status Quo in der Region OstWestfalenLippe}}},
  doi          = {{10.55594/tmao3234}},
  year         = {{2022}},
}

@article{33701,
  abstract     = {{<jats:title>Abstract</jats:title>
               <jats:p>Künstliche Intelligenz bietet großes Potenzial im Engineering. Der Einsatz gestattet insbesondere für Wissensarbeiter eine effiziente Arbeitsteilung, in der beispielsweise fehleranfällige und repetitive Aktivitäten unterstützt werden. Eine erfolgreiche Einführung bedarf einer vorangehenden Analyse von nutzenstiftenden Einsatzpotenzialen, bei der alle Anwendenden frühzeitig einbezogen werden. Der folgende Beitrag verdeutlicht dieses Vorgehen anhand eines realen Beispiels im Sondermaschinenbau.</jats:p>}},
  author       = {{Kharatyan, Aschot and Humpert, Lynn and Anacker, Harald and Dumitrescu, Roman and Wäschle, Moritz and Albers, Albert and Horstmeyer, Sarah}},
  issn         = {{2511-0896}},
  journal      = {{Zeitschrift für wirtschaftlichen Fabrikbetrieb}},
  keywords     = {{Management Science and Operations Research, Strategy and Management, General Engineering}},
  number       = {{6}},
  pages        = {{427--431}},
  publisher    = {{Walter de Gruyter GmbH}},
  title        = {{{Künstliche Intelligenz im Engineering}}},
  doi          = {{10.1515/zwf-2022-1074}},
  volume       = {{117}},
  year         = {{2022}},
}

@article{33705,
  abstract     = {{<jats:title>Abstract</jats:title><jats:p>The ongoing digitalization of products offers product managers new potentials to plan future product generations based on data from the use phase instead of assumptions. However, product managers often face difficulties in identifying promising opportunities for analyzing use phase data. In this paper, we propose a method for planning the analysis of use phase data in product planning. It leads product managers from the identification of promising investigation needs to the derivation of specific use cases. The application of the method is shown using the example of a manufacturing company.</jats:p>}},
  author       = {{Meyer, Maurice and Wiederkehr, Ingrid and Koldewey, Christian and Dumitrescu, Roman}},
  issn         = {{2732-527X}},
  journal      = {{Proceedings of the Design Society}},
  pages        = {{753--762}},
  publisher    = {{Cambridge University Press (CUP)}},
  title        = {{{Planning the Analysis of Use Phase Data in Product Planning}}},
  doi          = {{10.1017/pds.2022.77}},
  volume       = {{2}},
  year         = {{2022}},
}

@inproceedings{33708,
  abstract     = {{The megatrend digitalization turns mechatronic products into continuous collectors and generators of use phase data. By analyzing this data, manufacturers can uncover valuable insights about the products and the users. Especially in product planning, these insights could be used to plan promising future product generations. The systematic exploitation of data analytics results, however, represents a serious challenge, as research on the topic is still scarce. In this paper, we present 13 design principles for exploiting data analytics results in product planning. The results are based on a systematic literature review and a workshop with a research consortium. The evaluation of the design principles is demonstrated with a real case of a manufacturing company. The identified design principles represent a first contribution to a still scarcely explored research field.}},
  author       = {{Meyer, Maurice and Fichtler, Timm and Koldewey, Christian and Dumitrescu, Roman}},
  booktitle    = {{ AMCIS 2022 Proceedings}},
  location     = {{Minneapolis}},
  title        = {{{How can Data Analytics Results be Exploited in the Early Phase of Product Development? 13 Design Principles for Data-Driven Product Planning}}},
  year         = {{2022}},
}