[{"page":"541-560","status":"public","year":"2026","author":[{"id":"86180","first_name":"Jan-Phillip","full_name":"Herrmann, Jan-Phillip","last_name":"Herrmann"},{"last_name":"Tackenberg","first_name":"Sven","full_name":"Tackenberg, Sven","id":"71470"},{"first_name":"Tharsika Pakeerathan","full_name":"Srirajan, Tharsika Pakeerathan","last_name":"Srirajan"},{"first_name":"Verena","last_name":"Nitsch","full_name":"Nitsch, Verena"}],"volume":84,"citation":{"din1505-2-1":"Herrmann, Jan-Phillip ; Tackenberg, Sven ; Srirajan, Tharsika Pakeerathan ; Nitsch, Verena: Incorporating scheduling autonomy of workers into flexible job shop scheduling: Learning and balancing decentralized task sequencing decisions with overall scheduling performance. In: Journal of Manufacturing Systems Bd. 84. Amsterdam, Elsevier BV (2026), Nr. 2, S. 541–560","chicago-de":"Herrmann, Jan-Phillip, Sven Tackenberg, Tharsika Pakeerathan Srirajan und Verena Nitsch. 2026. Incorporating scheduling autonomy of workers into flexible job shop scheduling: Learning and balancing decentralized task sequencing decisions with overall scheduling performance. Journal of Manufacturing Systems 84, Nr. 2: 541–560. doi:10.1016/j.jmsy.2025.12.020, .","short":"J.-P. Herrmann, S. Tackenberg, T.P. Srirajan, V. Nitsch, Journal of Manufacturing Systems 84 (2026) 541–560.","chicago":"Herrmann, Jan-Phillip, Sven Tackenberg, Tharsika Pakeerathan Srirajan, and Verena Nitsch. “Incorporating Scheduling Autonomy of Workers into Flexible Job Shop Scheduling: Learning and Balancing Decentralized Task Sequencing Decisions with Overall Scheduling Performance.” Journal of Manufacturing Systems 84, no. 2 (2026): 541–60. https://doi.org/10.1016/j.jmsy.2025.12.020.","apa":"Herrmann, J.-P., Tackenberg, S., Srirajan, T. P., & Nitsch, V. (2026). Incorporating scheduling autonomy of workers into flexible job shop scheduling: Learning and balancing decentralized task sequencing decisions with overall scheduling performance. Journal of Manufacturing Systems, 84(2), 541–560. https://doi.org/10.1016/j.jmsy.2025.12.020","ufg":"Herrmann, Jan-Phillip u. a.: Incorporating scheduling autonomy of workers into flexible job shop scheduling: Learning and balancing decentralized task sequencing decisions with overall scheduling performance, in: Journal of Manufacturing Systems 84 (2026), H. 2, S. 541–560.","mla":"Herrmann, Jan-Phillip, et al. “Incorporating Scheduling Autonomy of Workers into Flexible Job Shop Scheduling: Learning and Balancing Decentralized Task Sequencing Decisions with Overall Scheduling Performance.” Journal of Manufacturing Systems, vol. 84, no. 2, 2026, pp. 541–60, https://doi.org/10.1016/j.jmsy.2025.12.020.","bjps":"Herrmann J-P et al. (2026) Incorporating Scheduling Autonomy of Workers into Flexible Job Shop Scheduling: Learning and Balancing Decentralized Task Sequencing Decisions with Overall Scheduling Performance. Journal of Manufacturing Systems 84, 541–560.","havard":"J.-P. Herrmann, S. Tackenberg, T.P. Srirajan, V. Nitsch, Incorporating scheduling autonomy of workers into flexible job shop scheduling: Learning and balancing decentralized task sequencing decisions with overall scheduling performance, Journal of Manufacturing Systems. 84 (2026) 541–560.","van":"Herrmann JP, Tackenberg S, Srirajan TP, Nitsch V. Incorporating scheduling autonomy of workers into flexible job shop scheduling: Learning and balancing decentralized task sequencing decisions with overall scheduling performance. Journal of Manufacturing Systems. 2026;84(2):541–60.","ieee":"J.-P. Herrmann, S. Tackenberg, T. P. Srirajan, and V. Nitsch, “Incorporating scheduling autonomy of workers into flexible job shop scheduling: Learning and balancing decentralized task sequencing decisions with overall scheduling performance,” Journal of Manufacturing Systems, vol. 84, no. 2, pp. 541–560, 2026, doi: 10.1016/j.jmsy.2025.12.020.","ama":"Herrmann JP, Tackenberg S, Srirajan TP, Nitsch V. Incorporating scheduling autonomy of workers into flexible job shop scheduling: Learning and balancing decentralized task sequencing decisions with overall scheduling performance. Journal of Manufacturing Systems. 2026;84(2):541-560. doi:10.1016/j.jmsy.2025.12.020"},"title":"Incorporating scheduling autonomy of workers into flexible job shop scheduling: Learning and balancing decentralized task sequencing decisions with overall scheduling performance","language":[{"iso":"eng"}],"keyword":["Human-centered scheduling","Job autonomy","Learning-to-rank","Flexible job shop scheduling","Human decision-making","Explainable artificial intelligence"],"date_updated":"2026-01-12T08:54:27Z","date_created":"2026-01-12T08:29:09Z","publication_identifier":{"issn":["0278-6125"]},"publication":"Journal of Manufacturing Systems","quality_controlled":"1","publisher":"Elsevier BV","abstract":[{"text":"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.","lang":"eng"}],"issue":"2","department":[{"_id":"DEP7027"}],"article_type":"original","publication_status":"published","doi":"10.1016/j.jmsy.2025.12.020","_id":"13337","place":"Amsterdam","intvolume":" 84","user_id":"83781","type":"scientific_journal_article"},{"title":"At the Brink of a Project : Heuristics to Block Potential Project Disasters during the Early Project Opportunity Screening","citation":{"havard":"Joana Geraldi, V. Stingl, M. Schriewersmann, At the Brink of a Project : Heuristics to Block Potential Project Disasters during the Early Project Opportunity Screening, in: L.A. Ika, J.K. Pinto (Eds.), Cambridge Handbook of Project Behavior , Cambridge University Press, Cambridge, 2025: pp. 216–232.","mla":"Geraldi, Joana , et al. “At the Brink of a Project : Heuristics to Block Potential Project Disasters during the Early Project Opportunity Screening.” Cambridge Handbook of Project Behavior , edited by Lavagnon A. Ika and Jeffrey K. Pinto, Cambridge University Press, 2025, pp. 216–32, https://doi.org/10.1017/9781009322737.","bjps":"Geraldi Joana , Stingl V and Schriewersmann M (2025) At the Brink of a Project : Heuristics to Block Potential Project Disasters during the Early Project Opportunity Screening. In Ika LA and Pinto JK (eds), Cambridge Handbook of Project Behavior . Cambridge: Cambridge University Press, pp. 216–232.","ufg":"Geraldi, Joana /Stingl, Verena/Schriewersmann, Maximilian: At the Brink of a Project : Heuristics to Block Potential Project Disasters during the Early Project Opportunity Screening, in: Ika, Lavagnon A./Pinto, Jeffrey K. (Hgg.): Cambridge handbook of project behavior , Cambridge 2025, S. 216–232.","ama":"Geraldi Joana , Stingl V, Schriewersmann M. At the Brink of a Project : Heuristics to Block Potential Project Disasters during the Early Project Opportunity Screening. In: Ika LA, Pinto JK, eds. Cambridge Handbook of Project Behavior . Cambridge University Press; 2025:216-232. doi:10.1017/9781009322737","van":"Geraldi Joana , Stingl V, Schriewersmann M. At the Brink of a Project : Heuristics to Block Potential Project Disasters during the Early Project Opportunity Screening. In: Ika LA, Pinto JK, editors. Cambridge handbook of project behavior . Cambridge: Cambridge University Press; 2025. p. 216–32.","chicago":"Geraldi, Joana , Verena Stingl, and Maximilian Schriewersmann. “At the Brink of a Project : Heuristics to Block Potential Project Disasters during the Early Project Opportunity Screening.” In Cambridge Handbook of Project Behavior , edited by Lavagnon A. Ika and Jeffrey K. Pinto, 216–32. Cambridge: Cambridge University Press, 2025. https://doi.org/10.1017/9781009322737.","ieee":"Joana Geraldi, V. Stingl, and M. Schriewersmann, “At the Brink of a Project : Heuristics to Block Potential Project Disasters during the Early Project Opportunity Screening,” in Cambridge handbook of project behavior , L. A. Ika and J. K. Pinto, Eds. Cambridge: Cambridge University Press, 2025, pp. 216–232. doi: 10.1017/9781009322737.","apa":"Geraldi, Joana , Stingl, V., & Schriewersmann, M. (2025). At the Brink of a Project : Heuristics to Block Potential Project Disasters during the Early Project Opportunity Screening. In L. A. Ika & J. K. Pinto (Eds.), Cambridge handbook of project behavior (pp. 216–232). Cambridge University Press. https://doi.org/10.1017/9781009322737","chicago-de":"Geraldi, Joana , Verena Stingl und Maximilian Schriewersmann. 2025. At the Brink of a Project : Heuristics to Block Potential Project Disasters during the Early Project Opportunity Screening. In: Cambridge handbook of project behavior , hg. von Lavagnon A. Ika und Jeffrey K. Pinto, 216–232. Cambridge: Cambridge University Press. doi:10.1017/9781009322737, .","din1505-2-1":"Geraldi, Joana ; Stingl, Verena ; Schriewersmann, Maximilian: At the Brink of a Project : Heuristics to Block Potential Project Disasters during the Early Project Opportunity Screening. In: Ika, L. A. ; Pinto, J. K. (Hrsg.): Cambridge handbook of project behavior . Cambridge : Cambridge University Press, 2025, S. 216–232","short":"Joana Geraldi, V. Stingl, M. Schriewersmann, in: L.A. Ika, J.K. Pinto (Eds.), Cambridge Handbook of Project Behavior , Cambridge University Press, Cambridge, 2025, pp. 216–232."},"publication_status":"published","year":"2025","author":[{"full_name":"Geraldi, Joana ","last_name":"Geraldi","first_name":" Joana "},{"first_name":"Verena ","full_name":"Stingl, Verena ","last_name":"Stingl"},{"id":"79121","full_name":"Schriewersmann, Maximilian","first_name":"Maximilian","last_name":"Schriewersmann"}],"department":[{"_id":"DEP1100"}],"abstract":[{"lang":"eng","text":"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."}],"status":"public","page":"216 - 232","publisher":"Cambridge University Press","publication_identifier":{"isbn":["9781009322768","9781009322775"],"eisbn":["9781009322737"]},"publication":"Cambridge handbook of project behavior ","type":"book_chapter","date_created":"2026-03-03T10:55:34Z","user_id":"83781","date_updated":"2026-03-03T12:03:38Z","editor":[{"full_name":"Ika, Lavagnon A.","first_name":"Lavagnon A.","last_name":"Ika"},{"full_name":"Pinto, Jeffrey K.","first_name":"Jeffrey K.","last_name":"Pinto"}],"keyword":["heuristics","decision making","project behaviour","hiding hand","bid/no-bid decision"],"place":"Cambridge","language":[{"iso":"eng"}],"_id":"13462","doi":"10.1017/9781009322737"},{"type":"scientific_journal_article","user_id":"83781","intvolume":" 11","place":"New York, NY","doi":"10.1109/access.2023.3340626","_id":"11445","isi":"1","publication_status":"published","department":[{"_id":"DEP7000"},{"_id":"DEP7020"}],"abstract":[{"text":"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.","lang":"eng"}],"publisher":"IEEE","date_created":"2024-05-10T11:48:50Z","publication_identifier":{"eissn":["2169-3536"]},"publication":"IEEE Access","date_updated":"2025-06-26T07:53:23Z","language":[{"iso":"eng"}],"keyword":["Artificial intelligence","assistance system","human decision-making","manufacturing"],"title":"Predicting Human Decision-Making for Task Selection in Manufacturing: A Systematic Literature Review","citation":{"short":"J.-P. Herrmann, S. Tackenberg, V. Nitsch, IEEE Access 11 (2023) 141172–141191.","chicago-de":"Herrmann, Jan-Phillip, Sven Tackenberg und Verena Nitsch. 2023. Predicting Human Decision-Making for Task Selection in Manufacturing: A Systematic Literature Review. IEEE Access 11: 141172–141191. doi:10.1109/access.2023.3340626, .","din1505-2-1":"Herrmann, Jan-Phillip ; Tackenberg, Sven ; Nitsch, Verena: Predicting Human Decision-Making for Task Selection in Manufacturing: A Systematic Literature Review. In: IEEE Access Bd. 11. New York, NY, IEEE (2023), S. 141172–141191","apa":"Herrmann, J.-P., Tackenberg, S., & Nitsch, V. (2023). Predicting Human Decision-Making for Task Selection in Manufacturing: A Systematic Literature Review. IEEE Access, 11, 141172–141191. https://doi.org/10.1109/access.2023.3340626","chicago":"Herrmann, Jan-Phillip, Sven Tackenberg, and Verena Nitsch. “Predicting Human Decision-Making for Task Selection in Manufacturing: A Systematic Literature Review.” IEEE Access 11 (2023): 141172–91. https://doi.org/10.1109/access.2023.3340626.","van":"Herrmann JP, Tackenberg S, Nitsch V. Predicting Human Decision-Making for Task Selection in Manufacturing: A Systematic Literature Review. IEEE Access. 2023;11:141172–91.","mla":"Herrmann, Jan-Phillip, et al. “Predicting Human Decision-Making for Task Selection in Manufacturing: A Systematic Literature Review.” IEEE Access, vol. 11, 2023, pp. 141172–91, https://doi.org/10.1109/access.2023.3340626.","havard":"J.-P. Herrmann, S. Tackenberg, V. Nitsch, Predicting Human Decision-Making for Task Selection in Manufacturing: A Systematic Literature Review, IEEE Access. 11 (2023) 141172–141191.","bjps":"Herrmann J-P, Tackenberg S and Nitsch V (2023) Predicting Human Decision-Making for Task Selection in Manufacturing: A Systematic Literature Review. IEEE Access 11, 141172–141191.","ufg":"Herrmann, Jan-Phillip/Tackenberg, Sven/Nitsch, Verena: Predicting Human Decision-Making for Task Selection in Manufacturing: A Systematic Literature Review, in: IEEE Access 11 (2023), S. 141172–141191.","ieee":"J.-P. Herrmann, S. Tackenberg, and V. Nitsch, “Predicting Human Decision-Making for Task Selection in Manufacturing: A Systematic Literature Review,” IEEE Access, vol. 11, pp. 141172–141191, 2023, doi: 10.1109/access.2023.3340626.","ama":"Herrmann JP, Tackenberg S, Nitsch V. Predicting Human Decision-Making for Task Selection in Manufacturing: A Systematic Literature Review. IEEE Access. 2023;11:141172-141191. doi:10.1109/access.2023.3340626"},"volume":11,"year":"2023","author":[{"id":"75846","full_name":"Herrmann, Jan-Phillip","last_name":"Herrmann","first_name":"Jan-Phillip"},{"full_name":"Tackenberg, Sven","first_name":"Sven","last_name":"Tackenberg","id":"71470"},{"first_name":"Verena","last_name":"Nitsch","full_name":"Nitsch, Verena"}],"external_id":{"isi":["001130254900001"]},"page":"141172-141191","status":"public"},{"type":"conference_editor_article","user_id":"83781","place":"New York, NY, USA","_id":"12994","doi":"10.1145/3603163.3610576","series_title":"HT '23","publication_status":"published","department":[{"_id":"DEP8000"}],"conference":{"name":"34th ACM Conference on Hypertext and Social Media","location":"Rome, Italy","end_date":"2023-09-08","start_date":"2023-09-04"},"abstract":[{"lang":"eng","text":"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."}],"publisher":"ACM","publication_identifier":{"isbn":["979-8-4007-0232-7"]},"publication":"Proceedings of the 34th ACM Conference on Hypertext and Social Media","date_created":"2025-06-18T13:24:37Z","date_updated":"2025-06-18T13:29:06Z","keyword":["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"],"language":[{"iso":"eng"}],"title":"HUMAN’23: 6th Workshop on Human Factors in Hypertext","citation":{"ufg":"Rubart, Jessica/Atzenbeck, Claus: HUMAN’23: 6th Workshop on Human Factors in Hypertext, New York, NY, USA 2023 (HT ’23).","havard":"J. Rubart, C. Atzenbeck, HUMAN’23: 6th Workshop on Human Factors in Hypertext, ACM, New York, NY, USA, 2023.","mla":"Rubart, Jessica, and Claus Atzenbeck. “HUMAN’23: 6th Workshop on Human Factors in Hypertext.” Proceedings of the 34th ACM Conference on Hypertext and Social Media, ACM, 2023, https://doi.org/10.1145/3603163.3610576.","bjps":"Rubart J and Atzenbeck C (2023) HUMAN’23: 6th Workshop on Human Factors in Hypertext. New York, NY, USA: ACM.","van":"Rubart J, Atzenbeck C. HUMAN’23: 6th Workshop on Human Factors in Hypertext. Proceedings of the 34th ACM Conference on Hypertext and Social Media. New York, NY, USA: ACM; 2023. (HT ’23).","ama":"Rubart J, Atzenbeck C. HUMAN’23: 6th Workshop on Human Factors in Hypertext. ACM; 2023. doi:10.1145/3603163.3610576","chicago":"Rubart, Jessica, and Claus Atzenbeck. HUMAN’23: 6th Workshop on Human Factors in Hypertext. Proceedings of the 34th ACM Conference on Hypertext and Social Media. HT ’23. New York, NY, USA: ACM, 2023. https://doi.org/10.1145/3603163.3610576.","apa":"Rubart, J., & Atzenbeck, C. (2023). HUMAN’23: 6th Workshop on Human Factors in Hypertext. In Proceedings of the 34th ACM Conference on Hypertext and Social Media. 34th ACM Conference on Hypertext and Social Media, Rome, Italy. ACM. https://doi.org/10.1145/3603163.3610576","ieee":"J. Rubart and C. Atzenbeck, HUMAN’23: 6th Workshop on Human Factors in Hypertext. New York, NY, USA: ACM, 2023. doi: 10.1145/3603163.3610576.","din1505-2-1":"Rubart, Jessica ; Atzenbeck, Claus: HUMAN’23: 6th Workshop on Human Factors in Hypertext, HT ’23. New York, NY, USA : ACM, 2023","chicago-de":"Rubart, Jessica und Claus Atzenbeck. 2023. HUMAN’23: 6th Workshop on Human Factors in Hypertext. Proceedings of the 34th ACM Conference on Hypertext and Social Media. HT ’23. New York, NY, USA: ACM. doi:10.1145/3603163.3610576, .","short":"J. Rubart, C. Atzenbeck, HUMAN’23: 6th Workshop on Human Factors in Hypertext, ACM, New York, NY, USA, 2023."},"year":"2023","author":[{"id":"45672","last_name":"Rubart","first_name":"Jessica","full_name":"Rubart, Jessica"},{"first_name":"Claus","last_name":"Atzenbeck","full_name":"Atzenbeck, Claus"}],"status":"public"},{"type":"book_chapter","user_id":"45673","has_accepted_license":"1","doi":"10.2777/244577","_id":"5817","place":"Brussels","file":[{"access_level":"open_access","content_type":"application/pdf","file_name":"KI0420586ENN.en.pdf","date_created":"2021-05-06T08:11:08Z","file_size":51133801,"file_id":"5818","date_updated":"2021-05-06T08:11:08Z","creator":"edu-dd0","relation":"main_file"}],"publication_status":"published","abstract":[{"lang":"eng","text":"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."}],"department":[{"_id":"DEP1303"},{"_id":"DEP1000"},{"_id":"DEP1055"}],"ddc":["710"],"quality_controlled":"1","publisher":"Publications Office of the European Union","edition":"1","date_created":"2021-05-06T08:13:04Z","publication":"Evaluating the impact of nature-based solutions. A handbook for practitioners","publication_identifier":{"eisbn":["978-92-76-22821-9"],"isbn":["978-92-76-22961-2"]},"file_date_updated":"2021-05-06T08:11:08Z","editor":[{"last_name":"Dumitru","first_name":"Adina","full_name":"Dumitru, Adina"},{"last_name":"Wendling","full_name":"Wendling, Laura","first_name":"Laura"}],"corporate_editor":["European Commission"],"date_updated":"2023-03-15T13:50:02Z","oa":"1","language":[{"iso":"eng"}],"keyword":["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"],"title":"Principles Guiding NBS Performance and Impact Evaluation","citation":{"chicago":"Skodra, Julita, Stuart Connop, Jean-Marc Tacnet, Nora Van Cauwenbergh, D. Almassy, C. Baldacchini, L. Basco Carrera, et al. “Principles Guiding NBS Performance and Impact Evaluation.” In Evaluating the Impact of Nature-Based Solutions. A Handbook for Practitioners, edited by Adina Dumitru, Laura Wendling, and European Commission, 1st ed., 46–69. Brussels: Publications Office of the European Union, 2021. https://doi.org/10.2777/244577.","ieee":"J. Skodra et al., “Principles Guiding NBS Performance and Impact Evaluation,” in Evaluating the impact of nature-based solutions. A handbook for practitioners, 1st ed., A. Dumitru, L. Wendling, and European Commission, Eds. Brussels: Publications Office of the European Union, 2021, pp. 46–69.","apa":"Skodra, J., Connop, S., Tacnet, J.-M., Van Cauwenbergh, N., Almassy, D., Baldacchini, C., … Vojinovic, Z. (2021). Principles Guiding NBS Performance and Impact Evaluation. In A. Dumitru, L. Wendling, & European Commission (Eds.), Evaluating the impact of nature-based solutions. A handbook for practitioners (1st ed., pp. 46–69). Brussels: Publications Office of the European Union. https://doi.org/10.2777/244577","havard":"J. Skodra, S. Connop, J.-M. Tacnet, N. Van Cauwenbergh, D. Almassy, C. Baldacchini, L. Basco Carrera, B. Caitana, M. Cardinali, E. Feliu, I. Garcia, G. Garcia-Blanco, G. Jones, L. Kraus, I. Mahmoud, S. Maia, E. Morello, B. Pérez Lapena, L. Pinter, F. Porcu, K. Reichborn-Kjennerud, L. Ruangpan, M. Rutzinger, Z. Vojinovic, Principles Guiding NBS Performance and Impact Evaluation, in: A. Dumitru, L. Wendling, European Commission (Eds.), Evaluating the Impact of Nature-Based Solutions. A Handbook for Practitioners, 1st ed., Publications Office of the European Union, Brussels, 2021: pp. 46–69.","bjps":"Skodra J et al. (2021) Principles Guiding NBS Performance and Impact Evaluation. In Dumitru A, Wendling L and European Commission (eds), Evaluating the Impact of Nature-Based Solutions. A Handbook for Practitioners, 1st ed. Brussels: Publications Office of the European Union, pp. 46–69.","mla":"Skodra, Julita, et al. “Principles Guiding NBS Performance and Impact Evaluation.” Evaluating the Impact of Nature-Based Solutions. A Handbook for Practitioners, edited by Adina Dumitru et al., 1st ed., Publications Office of the European Union, 2021, pp. 46–69, doi:10.2777/244577.","ufg":"Skodra, Julita et. al. (2021): Principles Guiding NBS Performance and Impact Evaluation, in: Adina Dumitru et. al. (Hgg.): Evaluating the impact of nature-based solutions. A handbook for practitioners, Brussels, 1, S. 46–69.","ama":"Skodra J, Connop S, Tacnet J-M, et al. Principles Guiding NBS Performance and Impact Evaluation. In: Dumitru A, Wendling L, European Commission, eds. Evaluating the Impact of Nature-Based Solutions. A Handbook for Practitioners. 1st ed. Brussels: Publications Office of the European Union; 2021:46-69. doi:10.2777/244577","van":"Skodra J, Connop S, Tacnet J-M, Van Cauwenbergh N, Almassy D, Baldacchini C, et al. Principles Guiding NBS Performance and Impact Evaluation. In: Dumitru A, Wendling L, European Commission, editors. Evaluating the impact of nature-based solutions A handbook for practitioners. 1st ed. Brussels: Publications Office of the European Union; 2021. p. 46–69.","din1505-2-1":"Skodra, Julita ; Connop, Stuart ; Tacnet, Jean-Marc ; Van Cauwenbergh, Nora ; Almassy, D. ; Baldacchini, C. ; Basco Carrera, L. ; Caitana, B. ; u. a.: Principles Guiding NBS Performance and Impact Evaluation. In: Dumitru, A. ; Wendling, L. ; European Commission (Hrsg.): Evaluating the impact of nature-based solutions. A handbook for practitioners. 1. Aufl. Brussels : Publications Office of the European Union, 2021, S. 46–69","chicago-de":"Skodra, Julita, Stuart Connop, Jean-Marc Tacnet, Nora Van Cauwenbergh, D. Almassy, C. 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Integrated reporting: The current state of empirical research, limitations and future research implications. Journal of Management Control. 2017;28:275–320.","chicago":"Velte, Patrick, and Martin Stawinoga. “Integrated Reporting: The Current State of Empirical Research, Limitations and Future Research Implications.” Journal of Management Control 28 (2017): 275–320. https://doi.org/10.1007/s00187-016-0235-4.","apa":"Velte, P., & Stawinoga, M. (2017). Integrated reporting: The current state of empirical research, limitations and future research implications. 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Elstermeyer, F.-J. Villmer, in: E. Padoano, F.-J. Villmer, Department of Production Engineering and Management (Eds.), Production Engineering and Management, Lemgo, 2017, pp. 101–113.","din1505-2-1":"Elstermeyer, O. ; Villmer, Franz-Josef: SLM Based Tooling for Injection Molding - Focus on Reduced Effort in Surface Quality Optimization. In: Padoano, E. ; Villmer, F.-J. ; Department of Production Engineering and Management (Hrsg.): Production Engineering and Management, Publication series in direct digital manufacturing . Lemgo, 2017, S. 101–113","chicago-de":"Elstermeyer, O. und Franz-Josef Villmer. 2017. SLM Based Tooling for Injection Molding - Focus on Reduced Effort in Surface Quality Optimization. In: Production Engineering and Management, hg. von Elio Padoano, Franz-Josef Villmer, und Department of Production Engineering and Management, 101–113. Publication series in direct digital manufacturing . Lemgo.","ama":"Elstermeyer O, Villmer F-J. SLM Based Tooling for Injection Molding - Focus on Reduced Effort in Surface Quality Optimization. In: Padoano E, Villmer F-J, Department of Production Engineering and Management, eds. Production Engineering and Management. Publication series in direct digital manufacturing . Lemgo; 2017:101-113.","van":"Elstermeyer O, Villmer F-J. SLM Based Tooling for Injection Molding - Focus on Reduced Effort in Surface Quality Optimization. In: Padoano E, Villmer F-J, Department of Production Engineering and Management, editors. Production Engineering and Management. Lemgo; 2017. p. 101–13. (Publication series in direct digital manufacturing ).","mla":"Elstermeyer, O., and Franz-Josef Villmer. “SLM Based Tooling for Injection Molding - Focus on Reduced Effort in Surface Quality Optimization.” Production Engineering and Management, edited by Elio Padoano et al., no. 1, 2017, pp. 101–13.","havard":"O. Elstermeyer, F.-J. Villmer, SLM Based Tooling for Injection Molding - Focus on Reduced Effort in Surface Quality Optimization, in: E. Padoano, F.-J. Villmer, Department of Production Engineering and Management (Eds.), Production Engineering and Management, Lemgo, 2017: pp. 101–113.","bjps":"Elstermeyer O and Villmer F-J (2017) SLM Based Tooling for Injection Molding - Focus on Reduced Effort in Surface Quality Optimization. In Padoano E, Villmer F-J and Department of Production Engineering and Management (eds), Production Engineering and Management. Lemgo, pp. 101–113.","ufg":"Elstermeyer, O./Villmer, Franz-Josef (2017): SLM Based Tooling for Injection Molding - Focus on Reduced Effort in Surface Quality Optimization, in: Elio Padoano et. al. (Hgg.): Production Engineering and Management (=Publication series in direct digital manufacturing ), Lemgo, S. 101–113.","ieee":"O. Elstermeyer and F.-J. Villmer, “SLM Based Tooling for Injection Molding - Focus on Reduced Effort in Surface Quality Optimization,” in Production Engineering and Management, Pordenone, Italy, 2017, no. 1, pp. 101–113.","apa":"Elstermeyer, O., & Villmer, F.-J. (2017). SLM Based Tooling for Injection Molding - Focus on Reduced Effort in Surface Quality Optimization. In E. Padoano, F.-J. Villmer, & Department of Production Engineering and Management (Eds.), Production Engineering and Management (pp. 101–113). Lemgo.","chicago":"Elstermeyer, O., and Franz-Josef Villmer. “SLM Based Tooling for Injection Molding - Focus on Reduced Effort in Surface Quality Optimization.” In Production Engineering and Management, edited by Elio Padoano, Franz-Josef Villmer, and Department of Production Engineering and Management, 101–13. Publication Series in Direct Digital Manufacturing . Lemgo, 2017."},"title":"SLM Based Tooling for Injection Molding - Focus on Reduced Effort in Surface Quality Optimization","place":"Lemgo","_id":"575","user_id":"45673","type":"conference","department":[{"_id":"DEP1306"}],"conference":{"name":"Proceedings7th International Conference","location":"Pordenone, Italy","start_date":"2017-09-28","end_date":"2017-09-29"},"issue":"1","abstract":[{"lang":"eng","text":"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."}],"publication_status":"published","series_title":"Publication series in direct digital manufacturing "},{"page":"357-376","status":"public","year":2016,"author":[{"full_name":"Robert, Sebastian","last_name":"Robert","first_name":"Sebastian"},{"full_name":"Büttner, Sebastian","last_name":"Büttner","id":"61868","first_name":"Sebastian"},{"last_name":"Röcker","first_name":"Carsten","id":"61525","full_name":"Röcker, Carsten"},{"first_name":"Andreas","last_name":"Holzinger","full_name":"Holzinger, Andreas"}],"citation":{"van":"Robert S, Büttner S, Röcker C, Holzinger A. Reasoning Under Uncertainty: Towards Collaborative Interactive Machine Learning. In: Holzinger A, editor. Machine Learning for Health Informatics : State-of-the-Art and Future Challenges . Cham, CH: Springer; 2016. p. 357–76. (Lecture Notes in Computer Science / Lecture Notes in Artificial Intelligence ; vol. 9605).","bjps":"Robert S et al. (2016) Reasoning Under Uncertainty: Towards Collaborative Interactive Machine Learning. In Holzinger A (ed.), Machine Learning for Health Informatics : State-of-the-Art and Future Challenges , vol. 9605. Cham, CH: Springer, pp. 357–376.","mla":"Robert, Sebastian, et al. “Reasoning Under Uncertainty: Towards Collaborative Interactive Machine Learning.” Machine Learning for Health Informatics : State-of-the-Art and Future Challenges , edited by Andreas Holzinger, vol. 9605, Springer, 2016, pp. 357–76, doi:10.1007/978-3-319-50478-0_18.","havard":"S. Robert, S. Büttner, C. Röcker, A. Holzinger, Reasoning Under Uncertainty: Towards Collaborative Interactive Machine Learning, in: A. Holzinger (Ed.), Machine Learning for Health Informatics : State-of-the-Art and Future Challenges , Springer, Cham, CH, 2016: pp. 357–376.","ufg":"Robert, Sebastian et. al. (2016): Reasoning Under Uncertainty: Towards Collaborative Interactive Machine Learning, in: Andreas Holzinger (Hg.): Machine Learning for Health Informatics : State-of-the-Art and Future Challenges (=Lecture Notes in Computer Science / Lecture Notes in Artificial Intelligence 9605), Cham, CH, S. 357–376.","apa":"Robert, S., Büttner, S., Röcker, C., & Holzinger, A. (2016). Reasoning Under Uncertainty: Towards Collaborative Interactive Machine Learning. In A. Holzinger (Ed.), Machine Learning for Health Informatics : State-of-the-Art and Future Challenges (Vol. 9605, pp. 357–376). Cham, CH: Springer. https://doi.org/10.1007/978-3-319-50478-0_18","chicago":"Robert, Sebastian, Sebastian Büttner, Carsten Röcker, and Andreas Holzinger. “Reasoning Under Uncertainty: Towards Collaborative Interactive Machine Learning.” In Machine Learning for Health Informatics : State-of-the-Art and Future Challenges , edited by Andreas Holzinger, 9605:357–76. Lecture Notes in Computer Science / Lecture Notes in Artificial Intelligence . Cham, CH: Springer, 2016. https://doi.org/10.1007/978-3-319-50478-0_18.","short":"S. Robert, S. Büttner, C. Röcker, A. Holzinger, in: A. Holzinger (Ed.), Machine Learning for Health Informatics : State-of-the-Art and Future Challenges , Springer, Cham, CH, 2016, pp. 357–376.","chicago-de":"Robert, Sebastian, Sebastian Büttner, Carsten Röcker und Andreas Holzinger. 2016. Reasoning Under Uncertainty: Towards Collaborative Interactive Machine Learning. In: Machine Learning for Health Informatics : State-of-the-Art and Future Challenges , hg. von Andreas Holzinger, 9605:357–376. Lecture Notes in Computer Science / Lecture Notes in Artificial Intelligence . Cham, CH: Springer. doi:10.1007/978-3-319-50478-0_18, .","din1505-2-1":"Robert, Sebastian ; Büttner, Sebastian ; Röcker, Carsten ; Holzinger, Andreas: Reasoning Under Uncertainty: Towards Collaborative Interactive Machine Learning. In: Holzinger, A. (Hrsg.): Machine Learning for Health Informatics : State-of-the-Art and Future Challenges , Lecture Notes in Computer Science / Lecture Notes in Artificial Intelligence . Bd. 9605. Cham, CH : Springer, 2016, S. 357–376","ama":"Robert S, Büttner S, Röcker C, Holzinger A. Reasoning Under Uncertainty: Towards Collaborative Interactive Machine Learning. In: Holzinger A, ed. Machine Learning for Health Informatics : State-of-the-Art and Future Challenges . Vol 9605. Lecture Notes in Computer Science / Lecture Notes in Artificial Intelligence . Cham, CH: Springer; 2016:357-376. doi:10.1007/978-3-319-50478-0_18","ieee":"S. Robert, S. Büttner, C. Röcker, and A. Holzinger, “Reasoning Under Uncertainty: Towards Collaborative Interactive Machine Learning,” in Machine Learning for Health Informatics : State-of-the-Art and Future Challenges , vol. 9605, A. Holzinger, Ed. Cham, CH: Springer, 2016, pp. 357–376."},"volume":9605,"title":"Reasoning Under Uncertainty: Towards Collaborative Interactive Machine Learning","language":[{"iso":"eng"}],"keyword":["Decision making","Reasoning","Interactive machine learning","Collaborative interactive machine learning"],"date_updated":"2023-03-15T13:49:51Z","editor":[{"full_name":"Holzinger, Andreas","first_name":"Andreas","last_name":"Holzinger"}],"date_created":"2020-12-22T14:11:00Z","publication_identifier":{"isbn":["978-3-319-50477-3 "],"eisbn":["978-3-319-50478-0 "]},"publication":"Machine Learning for Health Informatics : State-of-the-Art and Future Challenges ","publisher":"Springer","department":[{"_id":"DEP5023"}],"abstract":[{"text":"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.","lang":"eng"}],"publication_status":"published","series_title":"Lecture Notes in Computer Science / Lecture Notes in Artificial Intelligence ","place":"Cham, CH","doi":"10.1007/978-3-319-50478-0_18","_id":"4298","user_id":"15514","intvolume":" 9605","type":"book_chapter"},{"oa":"1","language":[{"iso":"eng"}],"keyword":["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"],"date_created":"2021-01-08T12:03:52Z","publication_identifier":{"isbn":["978-3-319-16225-6"],"eissn":["1611-3349"],"issn":["0302-9743"],"eisbn":["978-3-319-16226-3"]},"date_updated":"2023-03-15T13:49:52Z","editor":[{"last_name":"Holzinger","full_name":"Holzinger, Andreas","first_name":"Andreas"},{"id":"61525","last_name":"Röcker","full_name":"Röcker, Carsten","first_name":"Carsten"},{"first_name":"Martina","full_name":"Ziefle, Martina","last_name":"Ziefle"}],"main_file_link":[{"url":"http://www.springerlink.com/content/978-3-319-16226-3 ","open_access":"1"}],"year":2015,"page":"275","status":"public","title":"Smart Health: Open Problems and Future Challenges","citation":{"ama":"Holzinger A, Röcker C, Ziefle M, eds. Smart Health: Open Problems and Future Challenges. Vol 8700. Heidelberg: Springer; 2015. doi:10.1007/978-3-319-16226-3","ieee":"A. Holzinger, C. Röcker, and M. Ziefle, Eds., Smart Health: Open Problems and Future Challenges, vol. 8700. Heidelberg: Springer, 2015.","chicago-de":"Holzinger, Andreas, Carsten Röcker und Martina Ziefle, Hrsg. 2015. Smart Health: Open Problems and Future Challenges. Bd. 8700. Lecture Notes in Computer Science / Information Systems and Applications, incl. Internet/Web, and HCI. Heidelberg: Springer. doi:10.1007/978-3-319-16226-3, .","din1505-2-1":"Holzinger, A. ; Röcker, C. ; Ziefle, M. (Hrsg.): Smart Health: Open Problems and Future Challenges, Lecture Notes in Computer Science / Information Systems and Applications, incl. Internet/Web, and HCI. Bd. 8700. Heidelberg : Springer, 2015","short":"A. Holzinger, C. Röcker, M. Ziefle, eds., Smart Health: Open Problems and Future Challenges, Springer, Heidelberg, 2015.","havard":"A. Holzinger, C. Röcker, M. Ziefle, eds., Smart Health: Open Problems and Future Challenges, Springer, Heidelberg, 2015.","bjps":"Holzinger A, Röcker C and Ziefle M (eds) (2015) Smart Health: Open Problems and Future Challenges. Heidelberg: Springer.","mla":"Holzinger, Andreas, et al., editors. Smart Health: Open Problems and Future Challenges. Vol. 8700, Springer, 2015, doi:10.1007/978-3-319-16226-3.","ufg":"Holzinger, Andreas et. al. (Hgg.) (2015): Smart Health: Open Problems and Future Challenges (=Lecture Notes in Computer Science / Information Systems and Applications, incl. Internet/Web, and HCI 8700), Heidelberg.","van":"Holzinger A, Röcker C, Ziefle M, editors. Smart Health: Open Problems and Future Challenges. Heidelberg: Springer; 2015. 275 p. (Lecture Notes in Computer Science / Information Systems and Applications, incl. Internet/Web, and HCI; vol. 8700).","chicago":"Holzinger, Andreas, Carsten Röcker, and Martina Ziefle, eds. Smart Health: Open Problems and Future Challenges. Vol. 8700. Lecture Notes in Computer Science / Information Systems and Applications, Incl. Internet/Web, and HCI. Heidelberg: Springer, 2015. https://doi.org/10.1007/978-3-319-16226-3.","apa":"Holzinger, A., Röcker, C., & Ziefle, M. (Eds.). (2015). Smart Health: Open Problems and Future Challenges (Vol. 8700). Heidelberg: Springer. https://doi.org/10.1007/978-3-319-16226-3"},"volume":8700,"place":"Heidelberg","doi":"10.1007/978-3-319-16226-3","_id":"4336","type":"book_editor","user_id":"15514","intvolume":" 8700","department":[{"_id":"DEP5023"}],"abstract":[{"text":"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.\r\n\r\nThe 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.\r\n\r\nThe 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.","lang":"eng"}],"publisher":"Springer","series_title":"Lecture Notes in Computer Science / Information Systems and Applications, incl. Internet/Web, and HCI","publication_status":"published"},{"citation":{"havard":"H. Dörksen, V. Lohweg, Automated Fuzzy Classification with Combinatorial Refinement, in: 20th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA) Luxembourg, Sep 2015. , IEEE, Luxembourg, 2015.","bjps":"Dörksen H and Lohweg V (2015) Automated Fuzzy Classification with Combinatorial Refinement. 20th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA) Luxembourg, Sep 2015. . Luxembourg: IEEE.","mla":"Dörksen, Helene, and Volker Lohweg. “Automated Fuzzy Classification with Combinatorial Refinement.” 20th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA) Luxembourg, Sep 2015. , IEEE, 2015, doi: 10.1109/ETFA.2015.7301514.","ufg":"Dörksen, Helene/Lohweg, Volker (2015): Automated Fuzzy Classification with Combinatorial Refinement, in: 20th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA) Luxembourg, Sep 2015. , Luxembourg.","van":"Dörksen H, Lohweg V. Automated Fuzzy Classification with Combinatorial Refinement. In: 20th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA) Luxembourg, Sep 2015 . Luxembourg: IEEE; 2015.","chicago":"Dörksen, Helene, and Volker Lohweg. “Automated Fuzzy Classification with Combinatorial Refinement.” In 20th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA) Luxembourg, Sep 2015. . Luxembourg: IEEE, 2015. https://doi.org/ 10.1109/ETFA.2015.7301514.","apa":"Dörksen, H., & Lohweg, V. (2015). Automated Fuzzy Classification with Combinatorial Refinement. In 20th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA) Luxembourg, Sep 2015. . Luxembourg: IEEE. https://doi.org/ 10.1109/ETFA.2015.7301514","chicago-de":"Dörksen, Helene und Volker Lohweg. 2015. Automated Fuzzy Classification with Combinatorial Refinement. In: 20th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA) Luxembourg, Sep 2015. . Luxembourg: IEEE. doi: 10.1109/ETFA.2015.7301514, .","din1505-2-1":"Dörksen, Helene ; Lohweg, Volker: Automated Fuzzy Classification with Combinatorial Refinement. In: 20th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA) Luxembourg, Sep 2015. . Luxembourg : IEEE, 2015","short":"H. Dörksen, V. Lohweg, in: 20th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA) Luxembourg, Sep 2015. , IEEE, Luxembourg, 2015.","ama":"Dörksen H, Lohweg V. Automated Fuzzy Classification with Combinatorial Refinement. In: 20th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA) Luxembourg, Sep 2015. . Luxembourg: IEEE; 2015. doi: 10.1109/ETFA.2015.7301514","ieee":"H. Dörksen and V. Lohweg, “Automated Fuzzy Classification with Combinatorial Refinement,” in 20th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA) Luxembourg, Sep 2015. , 2015."},"title":"Automated Fuzzy Classification with Combinatorial Refinement","status":"public","publisher":"IEEE","year":2015,"author":[{"last_name":"Dörksen","id":"46416","full_name":"Dörksen, Helene","first_name":"Helene"},{"id":"1804","last_name":"Lohweg","full_name":"Lohweg, Volker","first_name":"Volker","orcid":"0000-0002-3325-7887"}],"department":[{"_id":"DEP5023"}],"abstract":[{"lang":"eng","text":"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."}],"user_id":"68554","date_updated":"2023-03-15T13:49:39Z","publication":"20th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA) Luxembourg, Sep 2015. ","publication_identifier":{"eisbn":[" 978-1-4673-7929-8"]},"date_created":"2019-12-04T09:23:21Z","type":"conference","keyword":["Support vector machines","Accuracy","Time complexity","Decision making","Motor drives","Shape","Sensors"],"place":"Luxembourg","language":[{"iso":"eng"}],"_id":"2136","doi":" 10.1109/ETFA.2015.7301514"},{"user_id":"45673","date_updated":"2023-03-15T13:49:39Z","corporate_editor":["IEEE"],"publication_identifier":{"eissn":["1946-0759 "],"isbn":["978-1-4799-0862-2"],"issn":["1946-0740 "],"eisbn":["978-1-4799-0864-6"]},"publication":"18th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA)","type":"conference","date_created":"2019-12-04T09:30:15Z","keyword":["Decision making","Robot sensing systems","Reliability","Production","Context","Fuzzy set theory","Data integration"],"place":"Cagliari, Italy","language":[{"iso":"eng"}],"_id":"2141","doi":"10.1109/ETFA.2013.6647984","citation":{"ama":"Mönks U, Lohweg V. Machine Conditioning by Importance Controlled Information Fusion. In: IEEE, ed. 18th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA). Cagliari, Italy; 2013. doi:10.1109/ETFA.2013.6647984","ieee":"U. Mönks and V. Lohweg, “Machine Conditioning by Importance Controlled Information Fusion,” in 18th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA), 2013.","short":"U. Mönks, V. Lohweg, in: IEEE (Ed.), 18th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA), Cagliari, Italy, 2013.","chicago-de":"Mönks, Uwe und Volker Lohweg. 2013. Machine Conditioning by Importance Controlled Information Fusion. In: 18th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA), hg. von IEEE. Cagliari, Italy. doi:10.1109/ETFA.2013.6647984, .","din1505-2-1":"Mönks, Uwe ; Lohweg, Volker: Machine Conditioning by Importance Controlled Information Fusion. In: IEEE (Hrsg.): 18th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA). Cagliari, Italy, 2013","van":"Mönks U, Lohweg V. Machine Conditioning by Importance Controlled Information Fusion. In: IEEE, editor. 18th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA). Cagliari, Italy; 2013.","ufg":"Mönks, Uwe/Lohweg, Volker (2013): Machine Conditioning by Importance Controlled Information Fusion, in: IEEE (Hg.): 18th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA), Cagliari, Italy.","mla":"Mönks, Uwe, and Volker Lohweg. “Machine Conditioning by Importance Controlled Information Fusion.” 18th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA), edited by IEEE, 2013, doi:10.1109/ETFA.2013.6647984.","havard":"U. Mönks, V. Lohweg, Machine Conditioning by Importance Controlled Information Fusion, in: IEEE (Ed.), 18th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA), Cagliari, Italy, 2013.","bjps":"Mönks U and Lohweg V (2013) Machine Conditioning by Importance Controlled Information Fusion. In IEEE (ed.), 18th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA). Cagliari, Italy.","apa":"Mönks, U., & Lohweg, V. (2013). Machine Conditioning by Importance Controlled Information Fusion. In IEEE (Ed.), 18th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA). Cagliari, Italy. https://doi.org/10.1109/ETFA.2013.6647984","chicago":"Mönks, Uwe, and Volker Lohweg. “Machine Conditioning by Importance Controlled Information Fusion.” In 18th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA), edited by IEEE. Cagliari, Italy, 2013. https://doi.org/10.1109/ETFA.2013.6647984."},"publication_status":"published","title":"Machine Conditioning by Importance Controlled Information Fusion","status":"public","author":[{"full_name":"Mönks, Uwe","id":"1825","last_name":"Mönks","first_name":"Uwe"},{"full_name":"Lohweg, Volker","id":"1804","last_name":"Lohweg","first_name":"Volker","orcid":"0000-0002-3325-7887"}],"year":2013,"department":[{"_id":"DEP5023"}],"abstract":[{"lang":"eng","text":"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."}]},{"extern":"1","title":"Methodology for the Analysis of Simulation-Based Decision-Making in the Manufacturing Area","publication_status":"published","citation":{"short":"G. Zülch, T.G. Gamber, P. Stock, in: J. Olhager, F. Persson (Eds.), Advances in Production Management Systems, Springer, New York, 2007, pp. 337–344.","din1505-2-1":"Zülch, Gert ; Gamber, Thilo Gerhard ; Stock, Patricia: Methodology for the Analysis of Simulation-Based Decision-Making in the Manufacturing Area. In: Olhager, J. ; Persson, F. (Hrsg.): Advances in Production Management Systems. New York : Springer, 2007, S. 337–344","chicago-de":"Zülch, Gert, Thilo Gerhard Gamber und Patricia Stock. 2007. Methodology for the Analysis of Simulation-Based Decision-Making in the Manufacturing Area. In: Advances in Production Management Systems, hg. von Jan Olhager und Fredrik Persson, 337–344. New York: Springer.","ieee":"G. Zülch, T. G. Gamber, and P. Stock, “Methodology for the Analysis of Simulation-Based Decision-Making in the Manufacturing Area,” in Advances in Production Management Systems, Linköping, 2007, pp. 337–344.","apa":"Zülch, G., Gamber, T. G., & Stock, P. (2007). Methodology for the Analysis of Simulation-Based Decision-Making in the Manufacturing Area. In J. Olhager & F. Persson (Eds.), Advances in Production Management Systems (pp. 337–344). New York: Springer.","chicago":"Zülch, Gert, Thilo Gerhard Gamber, and Patricia Stock. “Methodology for the Analysis of Simulation-Based Decision-Making in the Manufacturing Area.” In Advances in Production Management Systems, edited by Jan Olhager and Fredrik Persson, 337–44. New York: Springer, 2007.","ama":"Zülch G, Gamber TG, Stock P. Methodology for the Analysis of Simulation-Based Decision-Making in the Manufacturing Area. In: Olhager J, Persson F, eds. Advances in Production Management Systems. New York: Springer; 2007:337-344.","van":"Zülch G, Gamber TG, Stock P. Methodology for the Analysis of Simulation-Based Decision-Making in the Manufacturing Area. In: Olhager J, Persson F, editors. Advances in Production Management Systems. New York: Springer; 2007. p. 337–44.","bjps":"Zülch G, Gamber TG and Stock P (2007) Methodology for the Analysis of Simulation-Based Decision-Making in the Manufacturing Area. In Olhager J and Persson F (eds), Advances in Production Management Systems. New York: Springer, pp. 337–344.","havard":"G. Zülch, T.G. Gamber, P. Stock, Methodology for the Analysis of Simulation-Based Decision-Making in the Manufacturing Area, in: J. Olhager, F. Persson (Eds.), Advances in Production Management Systems, Springer, New York, 2007: pp. 337–344.","mla":"Zülch, Gert, et al. “Methodology for the Analysis of Simulation-Based Decision-Making in the Manufacturing Area.” Advances in Production Management Systems, edited by Jan Olhager and Fredrik Persson, Springer, 2007, pp. 337–44.","ufg":"Zülch, Gert et. al. (2007): Methodology for the Analysis of Simulation-Based Decision-Making in the Manufacturing Area, in: Jan Olhager/Fredrik Persson (Hgg.): Advances in Production Management Systems, New York, S. 337–344."},"conference":{"name":"International IFIP TC 5, WG 5.7 Conference on Advances in Production Management Systems (APMS 2007)","location":"Linköping","start_date":"2007-09-17","end_date":"2007-09-19"},"year":2007,"author":[{"last_name":"Zülch","first_name":"Gert","full_name":"Zülch, Gert"},{"id":"71451","last_name":"Gamber","full_name":"Gamber, Thilo Gerhard","first_name":"Thilo Gerhard"},{"last_name":"Stock","first_name":"Patricia","full_name":"Stock, Patricia"}],"abstract":[{"text":"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.","lang":"eng"}],"page":"337-344","status":"public","publisher":"Springer","type":"conference","date_created":"2019-01-30T07:51:48Z","publication_identifier":{"isbn":["978-0-387-74157-4"]},"publication":"Advances in Production Management Systems","date_updated":"2023-03-15T13:49:56Z","user_id":"74004","editor":[{"full_name":"Olhager, Jan","last_name":"Olhager","first_name":"Jan"},{"last_name":"Persson","first_name":"Fredrik","full_name":"Persson, Fredrik"}],"language":[{"iso":"eng"}],"place":"New York","keyword":["production planning and control","decision-making system","personnel-oriented simulation"],"_id":"486"}]