[{"publication_identifier":{"issn":["0278-6125"]},"volume":84,"intvolume":"        84","title":"Incorporating scheduling autonomy of workers into flexible job shop scheduling: Learning and balancing decentralized task sequencing decisions with overall scheduling performance","article_type":"original","status":"public","issue":"2","publication":"Journal of Manufacturing Systems","publisher":"Elsevier BV","author":[{"id":"86180","full_name":"Herrmann, Jan-Phillip","first_name":"Jan-Phillip","last_name":"Herrmann"},{"id":"71470","first_name":"Sven","full_name":"Tackenberg, Sven","last_name":"Tackenberg"},{"last_name":"Srirajan","first_name":"Tharsika Pakeerathan","full_name":"Srirajan, Tharsika Pakeerathan"},{"first_name":"Verena","full_name":"Nitsch, Verena","last_name":"Nitsch"}],"abstract":[{"lang":"eng","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."}],"publication_status":"published","year":"2026","citation":{"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.","bjps":"<b>Herrmann J-P <i>et al.</i></b> (2026) Incorporating Scheduling Autonomy of Workers into Flexible Job Shop Scheduling: Learning and Balancing Decentralized Task Sequencing Decisions with Overall Scheduling Performance. <i>Journal of Manufacturing Systems</i> <b>84</b>, 541–560.","ufg":"<b>Herrmann, Jan-Phillip u. a.</b>: Incorporating scheduling autonomy of workers into flexible job shop scheduling: Learning and balancing decentralized task sequencing decisions with overall scheduling performance, in: <i>Journal of Manufacturing Systems</i> 84 (2026), H. 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. <i>Journal of Manufacturing Systems</i> 84, Nr. 2: 541–560. doi:<a href=\"https://doi.org/10.1016/j.jmsy.2025.12.020\">10.1016/j.jmsy.2025.12.020</a>, .","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. <i>Journal of Manufacturing Systems</i>. 2026;84(2):541-560. doi:<a href=\"https://doi.org/10.1016/j.jmsy.2025.12.020\">10.1016/j.jmsy.2025.12.020</a>","short":"J.-P. Herrmann, S. Tackenberg, T.P. Srirajan, V. Nitsch, Journal of Manufacturing Systems 84 (2026) 541–560.","din1505-2-1":"<span style=\"font-variant:small-caps;\">Herrmann, Jan-Phillip</span> ; <span style=\"font-variant:small-caps;\">Tackenberg, Sven</span> ; <span style=\"font-variant:small-caps;\">Srirajan, Tharsika Pakeerathan</span> ; <span style=\"font-variant:small-caps;\">Nitsch, Verena</span>: Incorporating scheduling autonomy of workers into flexible job shop scheduling: Learning and balancing decentralized task sequencing decisions with overall scheduling performance. In: <i>Journal of Manufacturing Systems</i> Bd. 84. Amsterdam, Elsevier BV (2026), Nr. 2, S. 541–560","apa":"Herrmann, J.-P., Tackenberg, S., Srirajan, T. P., &#38; Nitsch, V. (2026). Incorporating scheduling autonomy of workers into flexible job shop scheduling: Learning and balancing decentralized task sequencing decisions with overall scheduling performance. <i>Journal of Manufacturing Systems</i>, <i>84</i>(2), 541–560. <a href=\"https://doi.org/10.1016/j.jmsy.2025.12.020\">https://doi.org/10.1016/j.jmsy.2025.12.020</a>","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,” <i>Journal of Manufacturing Systems</i>, vol. 84, no. 2, pp. 541–560, 2026, doi: <a href=\"https://doi.org/10.1016/j.jmsy.2025.12.020\">10.1016/j.jmsy.2025.12.020</a>.","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.” <i>Journal of Manufacturing Systems</i> 84, no. 2 (2026): 541–60. <a href=\"https://doi.org/10.1016/j.jmsy.2025.12.020\">https://doi.org/10.1016/j.jmsy.2025.12.020</a>.","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.” <i>Journal of Manufacturing Systems</i>, vol. 84, no. 2, 2026, pp. 541–60, <a href=\"https://doi.org/10.1016/j.jmsy.2025.12.020\">https://doi.org/10.1016/j.jmsy.2025.12.020</a>."},"type":"scientific_journal_article","date_updated":"2026-01-12T08:54:27Z","department":[{"_id":"DEP7027"}],"date_created":"2026-01-12T08:29:09Z","user_id":"83781","doi":"10.1016/j.jmsy.2025.12.020","page":"541-560","keyword":["Human-centered scheduling","Job autonomy","Learning-to-rank","Flexible job shop scheduling","Human decision-making","Explainable artificial intelligence"],"place":"Amsterdam","quality_controlled":"1","_id":"13337","language":[{"iso":"eng"}]},{"language":[{"iso":"eng"}],"_id":"13349","quality_controlled":"1","place":"Amsterdam","keyword":["Task Sequencing","Manufacturing","Learning To Rank","Scheduling Human Factors","Case Study"],"page":"1820-1829","doi":"10.1016/j.procs.2025.01.244","user_id":"83781","date_created":"2026-01-29T10:15:17Z","department":[{"_id":"DEP7020"}],"date_updated":"2026-02-10T10:54:05Z","type":"scientific_journal_article","citation":{"ama":"Herrmann JP, Tackenberg S, Burgert F, Nitsch V. Influencing factors on worker task sequencing decisions in a medium-sized hydraulic cylinder manufacturer. <i>Procedia Computer Science</i>. 2025;253:1820-1829. doi:<a href=\"https://doi.org/10.1016/j.procs.2025.01.244\">10.1016/j.procs.2025.01.244</a>","short":"J.-P. Herrmann, S. Tackenberg, F. Burgert, V. Nitsch, Procedia Computer Science 253 (2025) 1820–1829.","havard":"J.-P. Herrmann, S. Tackenberg, F. Burgert, V. Nitsch, Influencing factors on worker task sequencing decisions in a medium-sized hydraulic cylinder manufacturer, Procedia Computer Science. 253 (2025) 1820–1829.","chicago-de":"Herrmann, Jan-Phillip, Sven Tackenberg, Florens Burgert und Verena Nitsch. 2025. Influencing factors on worker task sequencing decisions in a medium-sized hydraulic cylinder manufacturer. <i>Procedia Computer Science</i> 253: 1820–1829. doi:<a href=\"https://doi.org/10.1016/j.procs.2025.01.244\">10.1016/j.procs.2025.01.244</a>, .","ufg":"<b>Herrmann, Jan-Phillip u. a.</b>: Influencing factors on worker task sequencing decisions in a medium-sized hydraulic cylinder manufacturer, in: <i>Procedia Computer Science</i> 253 (2025),  S. 1820–1829.","bjps":"<b>Herrmann J-P <i>et al.</i></b> (2025) Influencing Factors on Worker Task Sequencing Decisions in a Medium-Sized Hydraulic Cylinder Manufacturer. <i>Procedia Computer Science</i> <b>253</b>, 1820–1829.","ieee":"J.-P. Herrmann, S. Tackenberg, F. Burgert, and V. Nitsch, “Influencing factors on worker task sequencing decisions in a medium-sized hydraulic cylinder manufacturer,” <i>Procedia Computer Science</i>, vol. 253, pp. 1820–1829, 2025, doi: <a href=\"https://doi.org/10.1016/j.procs.2025.01.244\">10.1016/j.procs.2025.01.244</a>.","van":"Herrmann JP, Tackenberg S, Burgert F, Nitsch V. Influencing factors on worker task sequencing decisions in a medium-sized hydraulic cylinder manufacturer. Procedia Computer Science. 2025;253:1820–9.","chicago":"Herrmann, Jan-Phillip, Sven Tackenberg, Florens Burgert, and Verena Nitsch. “Influencing Factors on Worker Task Sequencing Decisions in a Medium-Sized Hydraulic Cylinder Manufacturer.” <i>Procedia Computer Science</i> 253 (2025): 1820–29. <a href=\"https://doi.org/10.1016/j.procs.2025.01.244\">https://doi.org/10.1016/j.procs.2025.01.244</a>.","mla":"Herrmann, Jan-Phillip, et al. “Influencing Factors on Worker Task Sequencing Decisions in a Medium-Sized Hydraulic Cylinder Manufacturer.” <i>Procedia Computer Science</i>, vol. 253, 2025, pp. 1820–29, <a href=\"https://doi.org/10.1016/j.procs.2025.01.244\">https://doi.org/10.1016/j.procs.2025.01.244</a>.","din1505-2-1":"<span style=\"font-variant:small-caps;\">Herrmann, Jan-Phillip</span> ; <span style=\"font-variant:small-caps;\">Tackenberg, Sven</span> ; <span style=\"font-variant:small-caps;\">Burgert, Florens</span> ; <span style=\"font-variant:small-caps;\">Nitsch, Verena</span>: Influencing factors on worker task sequencing decisions in a medium-sized hydraulic cylinder manufacturer. In: <i>Procedia Computer Science</i> Bd. 253. Amsterdam, Elsevier BV (2025), S. 1820–1829","apa":"Herrmann, J.-P., Tackenberg, S., Burgert, F., &#38; Nitsch, V. (2025). Influencing factors on worker task sequencing decisions in a medium-sized hydraulic cylinder manufacturer. <i>Procedia Computer Science</i>, <i>253</i>, 1820–1829. <a href=\"https://doi.org/10.1016/j.procs.2025.01.244\">https://doi.org/10.1016/j.procs.2025.01.244</a>"},"year":"2025","publication_status":"published","abstract":[{"text":"In weakly-structured work processes, workers are free to decide in which sequence to process their tasks. Predicting their decision-making helps plan production more accurately while preserving workers’ autonomy. The factors that influence workers’ decision-making depend on the manufacturing process and person considered, and they must be newly collected for each use case. This paper identifies the factors influencing workers when deciding in which sequence to process manufacturing tasks in a medium-sized hydraulic cylinder manufacturer. Five workers and two lead workers were observed and interviewed during several work shifts about influencing factors. The authors propose a new interview technique called indifference testing to overcome subjects’ difficulty articulating their decision-making process. Collected factors were categorized using inductive category formation and context analysis. The analyses identified 75 influencing factors comprising 37 decision attributes and 38 decision rules. The identified decision attributes indicate that worker preferences are influenced by attributes from the classical scheduling literature and attributes related to worker well-being, circadian rhythms, and ergonomics. The identified decision rules are useful constituents of more complex preference functions. The decision attributes and rules enable the construction of machine learning models to predict workers’ task sequencing decisions in job shops. Potential applications include systematically eliminating or controlling influencing factors through workplace design measures to increase worker well-being and optimality of their decisions.","lang":"eng"}],"author":[{"id":"86180","last_name":"Herrmann","first_name":"Jan-Phillip","full_name":"Herrmann, Jan-Phillip"},{"last_name":"Tackenberg","first_name":"Sven","full_name":"Tackenberg, Sven","id":"71470"},{"last_name":"Burgert","first_name":"Florens","full_name":"Burgert, Florens"},{"first_name":"Verena","full_name":"Nitsch, Verena","last_name":"Nitsch"}],"publisher":"Elsevier BV","publication":"Procedia Computer Science","status":"public","title":"Influencing factors on worker task sequencing decisions in a medium-sized hydraulic cylinder manufacturer","intvolume":"       253","volume":253,"publication_identifier":{"issn":["1877-0509"]}},{"conference":{"location":"Aachen","start_date":"2025-03-25","end_date":"2025-03-27","name":"71. Kongress der Gesellschaft für Arbeitswissenschaft e.V."},"type":"conference_editor_article","citation":{"ama":"Herrmann JP, Tackenberg S, Nitsch V. <i>Analyse Der Entscheidungsfindung von Fertigungsmitarbeitenden Durch Erklärbare Künstliche Intelligenz Zur Ableitung Arbeitsorganisatorischer Gestaltungsempfehlungen</i>. (Gesellschaft für Arbeitswissenschaft e.V. Sankt Augustin, ed.). GfA-Press; 2025:415-420. doi:<a href=\"https://doi.org/10.61063/FK2025\">10.61063/FK2025</a>","short":"J.-P. Herrmann, S. Tackenberg, V. Nitsch, Analyse Der Entscheidungsfindung von Fertigungsmitarbeitenden Durch Erklärbare Künstliche Intelligenz Zur Ableitung Arbeitsorganisatorischer Gestaltungsempfehlungen, GfA-Press, Sankt Augustin, 2025.","havard":"J.-P. Herrmann, S. Tackenberg, V. Nitsch, Analyse der Entscheidungsfindung von Fertigungsmitarbeitenden durch erklärbare künstliche Intelligenz zur Ableitung arbeitsorganisatorischer Gestaltungsempfehlungen, GfA-Press, Sankt Augustin, 2025.","bjps":"<b>Herrmann J-P, Tackenberg S and Nitsch V</b> (2025) <i>Analyse Der Entscheidungsfindung von Fertigungsmitarbeitenden Durch Erklärbare Künstliche Intelligenz Zur Ableitung Arbeitsorganisatorischer Gestaltungsempfehlungen</i>, Gesellschaft für Arbeitswissenschaft e.V. Sankt Augustin (ed.). Sankt Augustin: GfA-Press.","chicago-de":"Herrmann, Jan-Phillip, Sven Tackenberg und Verena Nitsch. 2025. <i>Analyse der Entscheidungsfindung von Fertigungsmitarbeitenden durch erklärbare künstliche Intelligenz zur Ableitung arbeitsorganisatorischer Gestaltungsempfehlungen</i>. Hg. von Gesellschaft für Arbeitswissenschaft e.V. Sankt Augustin. <i>Arbeit 5.0: Menschzentrierte Innovationen für die Zukunft der Arbeit</i>. Sankt Augustin: GfA-Press. doi:<a href=\"https://doi.org/10.61063/FK2025\">10.61063/FK2025</a>, .","ufg":"<b>Herrmann, Jan-Phillip/Tackenberg, Sven/Nitsch, Verena</b>: Analyse der Entscheidungsfindung von Fertigungsmitarbeitenden durch erklärbare künstliche Intelligenz zur Ableitung arbeitsorganisatorischer Gestaltungsempfehlungen, hg. von Gesellschaft für Arbeitswissenschaft e.V. Sankt Augustin, Sankt Augustin 2025.","chicago":"Herrmann, Jan-Phillip, Sven Tackenberg, and Verena Nitsch. <i>Analyse Der Entscheidungsfindung von Fertigungsmitarbeitenden Durch Erklärbare Künstliche Intelligenz Zur Ableitung Arbeitsorganisatorischer Gestaltungsempfehlungen</i>. Edited by Gesellschaft für Arbeitswissenschaft e.V. Sankt Augustin. <i>Arbeit 5.0: Menschzentrierte Innovationen Für Die Zukunft Der Arbeit</i>. Sankt Augustin: GfA-Press, 2025. <a href=\"https://doi.org/10.61063/FK2025\">https://doi.org/10.61063/FK2025</a>.","mla":"Herrmann, Jan-Phillip, et al. “Analyse Der Entscheidungsfindung von Fertigungsmitarbeitenden Durch Erklärbare Künstliche Intelligenz Zur Ableitung Arbeitsorganisatorischer Gestaltungsempfehlungen.” <i>Arbeit 5.0: Menschzentrierte Innovationen Für Die Zukunft Der Arbeit</i>, edited by Gesellschaft für Arbeitswissenschaft e.V. Sankt Augustin, GfA-Press, 2025, pp. 415–20, <a href=\"https://doi.org/10.61063/FK2025\">https://doi.org/10.61063/FK2025</a>.","ieee":"J.-P. Herrmann, S. Tackenberg, and V. Nitsch, <i>Analyse der Entscheidungsfindung von Fertigungsmitarbeitenden durch erklärbare künstliche Intelligenz zur Ableitung arbeitsorganisatorischer Gestaltungsempfehlungen</i>. Sankt Augustin: GfA-Press, 2025, pp. 415–420. doi: <a href=\"https://doi.org/10.61063/FK2025\">10.61063/FK2025</a>.","van":"Herrmann JP, Tackenberg S, Nitsch V. Analyse der Entscheidungsfindung von Fertigungsmitarbeitenden durch erklärbare künstliche Intelligenz zur Ableitung arbeitsorganisatorischer Gestaltungsempfehlungen. Gesellschaft für Arbeitswissenschaft e.V. Sankt Augustin, editor. Arbeit 5.0: Menschzentrierte Innovationen für die Zukunft der Arbeit. Sankt Augustin: GfA-Press; 2025.","din1505-2-1":"<span style=\"font-variant:small-caps;\">Herrmann, Jan-Phillip</span> ; <span style=\"font-variant:small-caps;\">Tackenberg, Sven</span> ; <span style=\"font-variant:small-caps;\">Nitsch, Verena</span> ; <span style=\"font-variant:small-caps;\">Gesellschaft für Arbeitswissenschaft e.V. Sankt Augustin</span> (Hrsg.): <i>Analyse der Entscheidungsfindung von Fertigungsmitarbeitenden durch erklärbare künstliche Intelligenz zur Ableitung arbeitsorganisatorischer Gestaltungsempfehlungen</i>. Sankt Augustin : GfA-Press, 2025","apa":"Herrmann, J.-P., Tackenberg, S., &#38; Nitsch, V. (2025). Analyse der Entscheidungsfindung von Fertigungsmitarbeitenden durch erklärbare künstliche Intelligenz zur Ableitung arbeitsorganisatorischer Gestaltungsempfehlungen. In Gesellschaft für Arbeitswissenschaft e.V. Sankt Augustin (Ed.), <i>Arbeit 5.0: Menschzentrierte Innovationen für die Zukunft der Arbeit</i> (pp. 415–420). GfA-Press. <a href=\"https://doi.org/10.61063/FK2025\">https://doi.org/10.61063/FK2025</a>"},"oa":"1","year":"2025","doi":"10.61063/FK2025","user_id":"83781","department":[{"_id":"DEP7020"}],"date_created":"2026-01-29T10:20:46Z","date_updated":"2026-02-10T12:24:29Z","main_file_link":[{"url":"https://www.gesellschaft-fuer-arbeitswissenschaft.de/publikationen_gfa-press-tagungsband.htm","open_access":"1"}],"page":"415-420","keyword":["Flexible Job Shop Scheduling","Learning To Rank","Erklärbare Künstliche Intelligenz","Planungsautonomie","Simulation"],"corporate_editor":["Gesellschaft für Arbeitswissenschaft e.V. Sankt Augustin"],"language":[{"iso":"eng"}],"_id":"13350","place":"Sankt Augustin","publication_identifier":{"eisbn":["978-3-936804-36-2"]},"title":"Analyse der Entscheidungsfindung von Fertigungsmitarbeitenden durch erklärbare künstliche Intelligenz zur Ableitung arbeitsorganisatorischer Gestaltungsempfehlungen","publication":"Arbeit 5.0: Menschzentrierte Innovationen für die Zukunft der Arbeit","status":"public","publication_status":"published","abstract":[{"lang":"ger","text":"In einer humanzentrierten Kleinserien- und Einzelfertigung mit Werkstattorganisation verfügen Fertigungsmitarbeitende häufig über eine hohe Autonomie und Entscheidungsfreiheit. Das Zusammenspiel individueller Planungsstrategien von Mitarbeitenden innerhalb eines Fertigungsprozesses kann sich positiv als auch negativ auf das Erreichen der produktionslogistischen Zielgrößen auswirken. In diesem Beitrag wird eine Variante des Flexible Job Shop Scheduling Problems vorgestellt, welches das Entscheidungsverhalten autonomer Arbeitspersonen bezüglich der Bearbeitungsreihenfolgebildung berücksichtigt. Weiterhin wird die Ableitung arbeitsorganisatorischer Gestaltungsempfehlungen durch die Analyse individueller Planungsstrategien von Arbeitspersonen mittels Methoden der erklärbaren künstlichen Intelligenz demonstriert. Betrachtungsgegenstand der Analyse ist die Entscheidung von Arbeitspersonen, in welcher Reihenfolge sie ihre täglichen Aufgaben abarbeiten. Der Beitrag schließt mit einer Diskussion über die Nutzung der vorgestellten Verfahren zur Ableitung von arbeitsorganisatorischen Gestaltungsempfehlungen."}],"author":[{"id":"75846","full_name":"Herrmann, Jan-Phillip","first_name":"Jan-Phillip","last_name":"Herrmann"},{"first_name":"Sven","full_name":"Tackenberg, Sven","last_name":"Tackenberg","id":"71470"},{"first_name":"Verena","full_name":"Nitsch, Verena","last_name":"Nitsch"}],"publisher":"GfA-Press"}]