{"place":"Amsterdam","date_updated":"2026-01-12T08:54:27Z","keyword":["Human-centered scheduling","Job autonomy","Learning-to-rank","Flexible job shop scheduling","Human decision-making","Explainable artificial intelligence"],"doi":"10.1016/j.jmsy.2025.12.020","quality_controlled":"1","issue":"2","page":"541-560","status":"public","publication_identifier":{"issn":["0278-6125"]},"citation":{"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","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.","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>.","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.","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.","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>.","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.","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>","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>, .","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>."},"publisher":"Elsevier BV","author":[{"full_name":"Herrmann, Jan-Phillip","last_name":"Herrmann","id":"86180","first_name":"Jan-Phillip"},{"id":"71470","first_name":"Sven","full_name":"Tackenberg, Sven","last_name":"Tackenberg"},{"first_name":"Tharsika Pakeerathan","last_name":"Srirajan","full_name":"Srirajan, Tharsika Pakeerathan"},{"last_name":"Nitsch","full_name":"Nitsch, Verena","first_name":"Verena"}],"publication_status":"published","volume":84,"date_created":"2026-01-12T08:29:09Z","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"}],"department":[{"_id":"DEP7027"}],"title":"Incorporating scheduling autonomy of workers into flexible job shop scheduling: Learning and balancing decentralized task sequencing decisions with overall scheduling performance","year":"2026","language":[{"iso":"eng"}],"article_type":"original","user_id":"83781","type":"scientific_journal_article","_id":"13337","publication":"Journal of Manufacturing Systems","intvolume":"        84"}