@misc{13337, abstract = {{In manufacturing systems with a job shop organization, queues between workstations create an intermittent process flow, allowing workers to schedule tasks entering the queue based on their needs and preferences. The resulting scheduling autonomy of individual workers often leads to inefficiencies in the overall production process due to the loss of control. Companies are therefore increasingly using algorithmic scheduling systems to assign task sequences to workers, thereby drastically reducing their autonomy and negatively affecting their job performance and well-being. This paper extends the existing flexible job shop scheduling problem by sequencing preferences (FJSPSP) to incorporate a human-centered perspective by predicting workers’ task sequencing decisions using learning-to-rank (LTR) methods. By learning workers’ individual task sequencing preferences, it becomes possible to predict the processing sequence based on task characteristics. The scheduling algorithm for the FJSPSP presented in the paper incorporates workers’ learned sequencing preferences as constraints. Considering workers’ learned task sequencing decisions, the FJSPSP optimizes only task assignments to maintain workers’ autonomy over task sequences. The contributions of this paper are fourfold, namely, (1) presenting an approach to elicit sequencing decision datasets from workers, (2) demonstrating the successful prediction of humans’ and an actual worker’s task sequencing decisions with LTR, (3) formulating the FJSPSP variant that integrates workers’ sequencing preferences as constraints and proving its effectiveness in a simulation study, and (4) consolidating these steps into an explainable artificial intelligence (XAI)- and LTR-enabled sociotechnical system design framework. The paper closes with a discussion of the overall methodology and future research perspectives.}}, author = {{Herrmann, Jan-Phillip and Tackenberg, Sven and Srirajan, Tharsika Pakeerathan and Nitsch, Verena}}, booktitle = {{Journal of Manufacturing Systems}}, issn = {{0278-6125}}, keywords = {{Human-centered scheduling, Job autonomy, Learning-to-rank, Flexible job shop scheduling, Human decision-making, Explainable artificial intelligence}}, number = {{2}}, pages = {{541--560}}, publisher = {{Elsevier BV}}, title = {{{Incorporating scheduling autonomy of workers into flexible job shop scheduling: Learning and balancing decentralized task sequencing decisions with overall scheduling performance}}}, doi = {{10.1016/j.jmsy.2025.12.020}}, volume = {{84}}, year = {{2026}}, } @inbook{13462, abstract = {{Traditional project management literature often portrays heuristics as flawed shortcuts leading to errors, advocating for rational, debiasing strategies to prevent cost overruns and benefit shortfalls. This is problematic as heuristics can be effective. Building on Gigerenzer’s concept of fast-and-frugal heuristics, this study examines the use of such smart heuristics by senior managers in a large engineering consultancy firm during the early bid/no-bid decision-making phase of infrastructure projects. Employing a qualitative method from the naturalistic decision-making program, the research uncovers a decision strategy termed "thresholding." This strategy distills extensive experience and interpretation of ambiguous information into binary decisions, effectively de-selecting projects that could be potentially disastrous. The approach also gives credence to agency, as it only deselects disasters but keeps many potential alternatives in the portfolio to mature into potentially ‘good projects’. At the same time, it addresses Flyvbjerg’s call for some scrutiny at the front end of projects to avoid catastrophic projects that start on the wrong premises. Our chapter adds to the debate on the Hiding Hand by not being concerned with the “hidden”, but instead, with what can be known in the early fuzzy front-end of projects.}}, author = {{Geraldi, Joana and Stingl, Verena and Schriewersmann, Maximilian}}, booktitle = {{Cambridge handbook of project behavior }}, editor = {{Ika, Lavagnon A. and Pinto, Jeffrey K.}}, isbn = {{9781009322768}}, keywords = {{heuristics, decision making, project behaviour, hiding hand, bid/no-bid decision}}, pages = {{216 -- 232}}, publisher = {{Cambridge University Press}}, title = {{{At the Brink of a Project : Heuristics to Block Potential Project Disasters during the Early Project Opportunity Screening}}}, doi = {{10.1017/9781009322737}}, year = {{2025}}, } @misc{11445, abstract = {{Predicting human decisions is a central challenge for planning and controlling production with weakly structured processes. Thus, workers’ decisions regarding the processing strategies and the temporal sequence of tasks to be processed are to be determined prospectively. Accordingly, there is a need to review methods for preference elicitation to develop individual predictive decision models. This paper presents a systematic literature review and discussion of 42 publications on predictive decision models and decision attributes. Methods for eliciting decision-making knowledge from manufacturing workers as part of the modeling process and decision model validation methods are reviewed and discussed in light of their predictive validity for individual task selection. The article synthesizes the recent literature for predicting human decision-making in manufacturing using artificial intelligence methods. Along with the review results, a future research agenda is proposed for modeling and simulating human decision-making in manufacturing. Knowledge about human preferences and the successful prediction of workers’ decision-making in manufacturing helps companies predict manufacturing objectives and derive organizational and work design measures.}}, author = {{Herrmann, Jan-Phillip and Tackenberg, Sven and Nitsch, Verena}}, booktitle = {{IEEE Access}}, issn = {{2169-3536}}, keywords = {{Artificial intelligence, assistance system, human decision-making, manufacturing}}, pages = {{141172--141191}}, publisher = {{IEEE}}, title = {{{Predicting Human Decision-Making for Task Selection in Manufacturing: A Systematic Literature Review}}}, doi = {{10.1109/access.2023.3340626}}, volume = {{11}}, year = {{2023}}, } @misc{12994, abstract = {{HUMAN 2023 is the 6th workshop of a series for the ACM Hypertext conferences. The HUMAN workshop has a strong focus on the user and thus is complementary to the strong machine analytics research direction that could be experienced in previous conferences.The user-centric view on hypertext not only includes user interfaces and interaction, but also discussions about hypertext application domains as well as human-centered AI. Furthermore, the workshop raises the question of how original hypertext ideas (e. g., Doug Engelbart’s "augmenting human intellect" [7] or Jeff Conklin’s "hypertext as a computer-based medium for thinking and communication" [6]) can improve today’s hypertext systems.}}, author = {{Rubart, Jessica and Atzenbeck, Claus}}, booktitle = {{Proceedings of the 34th ACM Conference on Hypertext and Social Media}}, isbn = {{979-8-4007-0232-7}}, keywords = {{user interfaces, information structuring, decision making, human-centered AI, cognitive aspects, scientific community, digital humanities, user interaction, human factors, user-centric, annotation, adaptive hypertext, hypermedia, collaboration, information systems, augmentation, hypertext, communication, intercultural aspects}}, location = {{Rome, Italy}}, publisher = {{ACM}}, title = {{{HUMAN’23: 6th Workshop on Human Factors in Hypertext}}}, doi = {{10.1145/3603163.3610576}}, year = {{2023}}, } @inbook{5817, abstract = {{The Handbook aims to provide decision-makers with a comprehensive NBS impact assessment framework, and a robust set of indicators and methodologies to assess impacts of nature-based solutions across 12 societal challenge areas: Climate Resilience; Water Management; Natural and Climate Hazards; Green Space Management; Biodiversity; Air Quality; Place Regeneration; Knowledge and Social Capacity Building for Sustainable Urban Transformation; Participatory Planning and Governance; Social Justice and Social Cohesion; Health and Well-being; New Economic Opportunities and Green Jobs. Indicators have been developed collaboratively by representatives of 17 individual EU-funded NBS projects and collaborating institutions such as the EEA and JRC, as part of the European Taskforce for NBS Impact Assessment, with the four-fold objective of: serving as a reference for relevant EU policies and activities; orient urban practitioners in developing robust impact evaluation frameworks for nature-based solutions at different scales; expand upon the pioneering work of the EKLIPSE framework by providing a comprehensive set of indicators and methodologies; and build the European evidence base regarding NBS impacts. They reflect the state of the art in current scientific research on impacts of nature-based solutions and valid and standardized methods of assessment, as well as the state of play in urban implementation of evaluation frameworks.}}, author = {{Skodra, Julita and Connop, Stuart and Tacnet, Jean-Marc and Van Cauwenbergh, Nora and Almassy, D. and Baldacchini, C. and Basco Carrera, L. and Caitana, B. and Cardinali, Marcel and Feliu, E. and Garcia, I. and Garcia-Blanco, G. and Jones, G. and Kraus, L. and Mahmoud, I. and Maia, S. and Morello, E. and Pérez Lapena, B. and Pinter, L. and Porcu, F. and Reichborn-Kjennerud, K. and Ruangpan, L. and Rutzinger, M. and Vojinovic, Z.}}, booktitle = {{Evaluating the impact of nature-based solutions. A handbook for practitioners}}, editor = {{Dumitru, Adina and Wendling, Laura}}, isbn = {{978-92-76-22961-2}}, keywords = {{atmospheric pollution, biodiversity, community resilience, database, decision-making, environmental impact, environmental indicator, environmental risk prevention, innovation, natural hazard, sustainable development, urban area, user guide, waste management}}, pages = {{46--69}}, publisher = {{Publications Office of the European Union}}, title = {{{Principles Guiding NBS Performance and Impact Evaluation}}}, doi = {{10.2777/244577}}, year = {{2021}}, } @inbook{5821, abstract = {{The Handbook aims to provide decision-makers with a comprehensive NBS impact assessment framework, and a robust set of indicators and methodologies to assess impacts of nature-based solutions across 12 societal challenge areas: Climate Resilience; Water Management; Natural and Climate Hazards; Green Space Management; Biodiversity; Air Quality; Place Regeneration; Knowledge and Social Capacity Building for Sustainable Urban Transformation; Participatory Planning and Governance; Social Justice and Social Cohesion; Health and Well-being; New Economic Opportunities and Green Jobs. Indicators have been developed collaboratively by representatives of 17 individual EU-funded NBS projects and collaborating institutions such as the EEA and JRC, as part of the European Taskforce for NBS Impact Assessment, with the four-fold objective of: serving as a reference for relevant EU policies and activities; orient urban practitioners in developing robust impact evaluation frameworks for nature-based solutions at different scales; expand upon the pioneering work of the EKLIPSE framework by providing a comprehensive set of indicators and methodologies; and build the European evidence base regarding NBS impacts. They reflect the state of the art in current scientific research on impacts of nature-based solutions and valid and standardized methods of assessment, as well as the state of play in urban implementation of evaluation frameworks.}}, author = {{Dumitru, Adina and Garcia, Igone and Zorita, Saioa and Tomé-Lourido, Davidé and Cardinali, Marcel and Feliu, E. and Fermoso, J. and Ferilli, G. and Guidolotti, G. and Hölscher, K. and Lodder, M. and Reichborn-Kjennerud, K. and Rinta-Hiiro, V. and Maia, S.}}, booktitle = {{Evaluating the impact of nature-based solutions. A handbook for practitioners}}, editor = {{Adina, Dumitru and Laura, Wendling}}, isbn = {{978-92-76-22961-2}}, keywords = {{atmospheric pollution, biodiversity, community resilience, database, decision-making, environmental impact, environmental indicator, environmental risk prevention, innovation, natural hazard, sustainable development, urban area, user guide, waste management}}, pages = {{78--104}}, publisher = {{Publications Office of the European Union}}, title = {{{Approaches to Monitoring and Evaluation Strategy Development}}}, doi = {{10.2777/244577}}, year = {{2021}}, } @inbook{5824, abstract = {{The Handbook aims to provide decision-makers with a comprehensive NBS impact assessment framework, and a robust set of indicators and methodologies to assess impacts of nature-based solutions across 12 societal challenge areas: Climate Resilience; Water Management; Natural and Climate Hazards; Green Space Management; Biodiversity; Air Quality; Place Regeneration; Knowledge and Social Capacity Building for Sustainable Urban Transformation; Participatory Planning and Governance; Social Justice and Social Cohesion; Health and Well-being; New Economic Opportunities and Green Jobs. Indicators have been developed collaboratively by representatives of 17 individual EU-funded NBS projects and collaborating institutions such as the EEA and JRC, as part of the European Taskforce for NBS Impact Assessment, with the four-fold objective of: serving as a reference for relevant EU policies and activities; orient urban practitioners in developing robust impact evaluation frameworks for nature-based solutions at different scales; expand upon the pioneering work of the EKLIPSE framework by providing a comprehensive set of indicators and methodologies; and build the European evidence base regarding NBS impacts. They reflect the state of the art in current scientific research on impacts of nature-based solutions and valid and standardized methods of assessment, as well as the state of play in urban implementation of evaluation frameworks.}}, author = {{Cardinali, Marcel}}, booktitle = {{Evaluating the Impact of Nature-based Solutions: Appendix of Methods}}, editor = {{Adina, Dumitru and Laura, Wendling}}, isbn = {{978-92-76-22960-5}}, keywords = {{atmospheric pollution, biodiversity, community resilience, database, decision-making, environmental impact, environmental indicator, environmental risk prevention, innovation, natural hazard, sustainable development, urban area, user guide, waste management}}, publisher = {{Publications Office of the European Union}}, title = {{{Contributors to Indicators of NBS Performance and Impact Assessment}}}, doi = {{10.2777/11361}}, year = {{2021}}, } @misc{11444, abstract = {{Generation Y marks the transition between a world with and without fully implemented Internet: A new kind of virtual space is formed and is impacting the way we live in this world, the way we perceive it, and the way we interact with it. Mobile devices, such as phones, build a new form of electronic technology type we interact with. This thesis investigates how this new form of relation can give insights to the way we are situated in this world with all its complexities and levels. In a case study, the author focuses on the relationship with mobile devices in the context of memory (making and recalling), materiality (haptic and metaphoric) and the human body itself (perception and sensory system). By developing a practice and contextualizing it in terms of space-theoretical and phenomenological concepts, this thesis aims to start a discourse on our human, (un-)conscious relation to mobile devices in place and time. Is it time for an imperfect, humane view towards the era of information from a Millennial perspective?}}, author = {{Pusch, Lisa}}, keywords = {{Memory Making, Generation Y, Millenials, Human-Computer-Relationship, Mobile Phone, Spatial Theory, Perception}}, pages = {{264}}, publisher = {{ProQuest}}, title = {{{On memory: Body, devices, material. Towards a new practice of MediaArchitecture}}}, year = {{2017}}, } @misc{7896, author = {{Velte, Patrick and Stawinoga, Martin}}, booktitle = {{Journal of Management Control}}, issn = {{2191-4761}}, keywords = {{Integrated reporting Legitimization theory Institutional theory Behavioural decision theory Resource dependency theory Empirical research}}, pages = {{275--320}}, publisher = {{Springer}}, title = {{{Integrated reporting: The current state of empirical research, limitations and future research implications}}}, doi = {{10.1007/s00187-016-0235-4}}, volume = {{28}}, year = {{2017}}, } @inproceedings{575, abstract = {{Additive manufacturing technologies can provide cost and time advantages in mold making, compared to traditional approaches. Nevertheless, their applicability is not yet completely proven, especially in terms of surface finishing. The aim of this research work is to create perfect mold inserts by Selective Laser Melting (SLM) and to optimize surface quality. Therefore a process is developed to reduce the effort of surface quality optimization including a high flexibility in design. The tested process shows that simple and affordable methods can lead to usable molds with only minor restrictions in terms of appearance. Due to the initial reduction of layer thicknesses and distinct settings of laser melting parameters, the surface smoothness is significantly enhanced during the SLM building process. Subsequently blasting, manual grinding, as well as polishing operations, enable a selective smoothening of the surface up to a polished finish. As a result, the built tool parts can be used instantly for injection molding.}}, author = {{Elstermeyer, O. and Villmer, Franz-Josef}}, booktitle = {{Production Engineering and Management}}, editor = {{Padoano, Elio and Villmer, Franz-Josef}}, isbn = {{978-3-946856-01-6}}, keywords = {{Tool making, Direct rapid tooling, Additively manufactured molds, Selective laser melting, Additive manufacturing process chain, Post-processing}}, location = {{Pordenone, Italy}}, number = {{1}}, pages = {{101--113}}, title = {{{SLM Based Tooling for Injection Molding - Focus on Reduced Effort in Surface Quality Optimization}}}, year = {{2017}}, } @inbook{4298, abstract = {{In this paper, we present the current state-of-the-art of decision making (DM) and machine learning (ML) and bridge the two research domains to create an integrated approach of complex problem solving based on human and computational agents. We present a novel classification of ML, emphasizing the human-in-the-loop in interactive ML (iML) and more specific on collaborative interactive ML (ciML), which we understand as a deep integrated version of iML, where humans and algorithms work hand in hand to solve complex problems. Both humans and computers have specific strengths and weaknesses and integrating humans into machine learning processes might be a very efficient way for tackling problems. This approach bears immense research potential for various domains, e.g., in health informatics or in industrial applications. We outline open questions and name future challenges that have to be addressed by the research community to enable the use of collaborative interactive machine learning for problem solving in a large scale.}}, author = {{Robert, Sebastian and Büttner, Sebastian and Röcker, Carsten and Holzinger, Andreas}}, booktitle = {{Machine Learning for Health Informatics : State-of-the-Art and Future Challenges }}, editor = {{Holzinger, Andreas}}, isbn = {{978-3-319-50477-3 }}, keywords = {{Decision making, Reasoning, Interactive machine learning, Collaborative interactive machine learning}}, pages = {{357--376}}, publisher = {{Springer}}, title = {{{Reasoning Under Uncertainty: Towards Collaborative Interactive Machine Learning}}}, doi = {{10.1007/978-3-319-50478-0_18}}, volume = {{9605}}, year = {{2016}}, } @book{4336, abstract = {{Prolonged life expectancy along with the increasing complexity of medicine and health services raises health costs worldwide dramatically. Whilst the smart health concept has much potential to support the concept of the emerging P4-medicine (preventive, participatory, predictive, and personalized), such high-tech medicine produces large amounts of high-dimensional, weakly-structured data sets and massive amounts of unstructured information. All these technological approaches along with “big data” are turning the medical sciences into a data-intensive science. To keep pace with the growing amounts of complex data, smart hospital approaches are a commandment of the future, necessitating context aware computing along with advanced interaction paradigms in new physical-digital ecosystems. The very successful synergistic combination of methodologies and approaches from Human-Computer Interaction (HCI) and Knowledge Discovery and Data Mining (KDD) offers ideal conditions for the vision to support human intelligence with machine learning. The papers selected for this volume focus on hot topics in smart health; they discuss open problems and future challenges in order to provide a research agenda to stimulate further research and progress.}}, editor = {{Holzinger, Andreas and Röcker, Carsten and Ziefle, Martina}}, isbn = {{978-3-319-16225-6}}, issn = {{1611-3349}}, keywords = {{HCI, ambient assisted living, big data, computational intelligence, context awareness, data centric medicine, decision support, interactive data mining, keyword detection, knoweldge bases, knoweldge discovery, machine learning, medical decision support, medical informatics, natural language processing, pervasive health, smart home, ubiquitous computing, visualization, wearable sensors}}, pages = {{275}}, publisher = {{Springer}}, title = {{{Smart Health: Open Problems and Future Challenges}}}, doi = {{10.1007/978-3-319-16226-3}}, volume = {{8700}}, year = {{2015}}, } @inproceedings{2136, abstract = {{In modern industrial applications driven by Cyber-physical systems (CPS) it is a challenging task to model and optimize processes such as machine analysis and diagnosis. Since the CPS have to act autonomously, a procedure for automated decision making has to be designed. In our work we concentrate on the design of a decision procedure by a fuzzy classifier approach. For our application on decision making in an industrial environment, a fuzzy approach was picked as convenient classification technique regarding balance between accuracy and computational time. We present a supervised learning method called FUZZY-ComRef which combines fuzzy classification and our combinatorial refinement method, called ComRef [1]. Due to the fact that fuzzy classification might behave inaccurately for some datasets, the aim of our approach is to improve the results provided by the (stand-alone) fuzzy classification. We show the performance of FUZZY-ComRef evaluated on the samples from the UCI Repository and on our real-world dataset Motor Drive Diagnosis. In addition, we discuss the quadratic computational time problem arising from the combinatorial nature of ComRef. Furthermore, we show based on real-time evaluations that within parallelisation the proposed FUZZY-ComRef is suitable to many applications in CPS.}}, author = {{Dörksen, Helene and Lohweg, Volker}}, booktitle = {{20th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA) Luxembourg, Sep 2015. }}, keywords = {{Support vector machines, Accuracy, Time complexity, Decision making, Motor drives, Shape, Sensors}}, publisher = {{IEEE}}, title = {{{Automated Fuzzy Classification with Combinatorial Refinement}}}, doi = {{ 10.1109/ETFA.2015.7301514}}, year = {{2015}}, } @inproceedings{2141, abstract = {{Sensor and information fusion is recently a major topic which becomes important in machine diagnosis and conditioning for complex production machines and process engineering. It is a known fact that distributed automation systems have a major impact on signal processing and pattern recognition for machine diagnosis. Therefore, it is necessary to research and develop smart diagnosis methods which are applicable for distributed systems like resource-limited cyber-physical systems. In this paper we propose an new approach for sensor and information fusion based on Evidence Theory and socio-psychological decision-making. We show that context based condition monitoring is instantiated even in conflict situations, oc-curing in real life scenarios permanently. A simple but effective importance measure is proposed which controls the significance of conditioning propositions in a system.}}, author = {{Mönks, Uwe and Lohweg, Volker}}, booktitle = {{18th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA)}}, isbn = {{978-1-4799-0862-2}}, issn = {{1946-0759 }}, keywords = {{Decision making, Robot sensing systems, Reliability, Production, Context, Fuzzy set theory, Data integration}}, title = {{{Machine Conditioning by Importance Controlled Information Fusion}}}, doi = {{10.1109/ETFA.2013.6647984}}, year = {{2013}}, } @inproceedings{486, abstract = {{Growing market demands on enterprises and the resulting challenges for their organization have been discussed for many years now. The flexibilty and mutability of an enterprise are thereby considered as a significant factor for success.}}, author = {{Zülch, Gert and Gamber, Thilo Gerhard and Stock, Patricia}}, booktitle = {{Advances in Production Management Systems}}, editor = {{Olhager, Jan and Persson, Fredrik}}, isbn = {{978-0-387-74157-4}}, keywords = {{production planning and control, decision-making system, personnel-oriented simulation}}, location = {{Linköping}}, pages = {{337--344}}, publisher = {{Springer}}, title = {{{Methodology for the Analysis of Simulation-Based Decision-Making in the Manufacturing Area}}}, year = {{2007}}, }