@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}},
}

@misc{13350,
  abstract     = {{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       = {{Herrmann, Jan-Phillip and Tackenberg, Sven and Nitsch, Verena}},
  booktitle    = {{Arbeit 5.0: Menschzentrierte Innovationen für die Zukunft der Arbeit}},
  keywords     = {{Flexible Job Shop Scheduling, Learning To Rank, Erklärbare Künstliche Intelligenz, Planungsautonomie, Simulation}},
  location     = {{Aachen}},
  pages        = {{415--420}},
  publisher    = {{GfA-Press}},
  title        = {{{Analyse der Entscheidungsfindung von Fertigungsmitarbeitenden durch erklärbare künstliche Intelligenz zur Ableitung arbeitsorganisatorischer Gestaltungsempfehlungen}}},
  doi          = {{10.61063/FK2025}},
  year         = {{2025}},
}

@misc{12834,
  abstract     = {{In the context of Industry 4.0, extensive deployment and application of advanced manufacturing equipment and various sensors is leading to a growing demand for data exchange between different devices. In smart factories, network transmission has multiprotocol features of wired/wireless communication, and different data flows have different real-time requirements. In this article, a heterogeneous network architecture based on software-defined network is proposed for realizing cross-network flexible forwarding of multisource manufacturing data and optimized utilization of network resources. Subsequently, the mechanism of cross-network fusion and scheduling (CNFS) is analyzed from the perspective of high dynamic characteristics and different delay requirements of data flows. Based on this analysis, a route-aware data flow dynamic reconstruction algorithm is proposed. The proposed algorithm improves the efficiency of manufacturing data cross-network fusion, especially for multivariety and small-batch intelligent manufacturing systems. Furthermore, for meeting the bandwidth requirements of different delay flows, a delay-sensitive network bandwidth scheduling algorithm is proposed. Finally, the effectiveness of the proposed CNFS mechanism is verified using a candy packaging intelligent production line prototype platform.}},
  author       = {{Wan, Jiafu and Yang, Jun and Wang, Shiyong and Li, Di and Li, Peng and Xia, Min}},
  booktitle    = {{IEEE Transactions on Industrial Informatics}},
  issn         = {{1941-0050}},
  keywords     = {{Heterogeneous networks, Real-time systems, Bandwidth, Job shop scheduling, Smart manufacturing, Computer architecture, Cross-network fusion, heterogeneous networks, network resource}},
  number       = {{9}},
  pages        = {{6059--6068}},
  publisher    = {{Institute of Electrical and Electronics Engineers (IEEE)}},
  title        = {{{Cross-Network Fusion and Scheduling for Heterogeneous Networks in Smart Factory}}},
  doi          = {{10.1109/tii.2019.2952669}},
  volume       = {{16}},
  year         = {{2019}},
}