In manufacturing, real-time decision support for production operations is critical to optimize efficiency, minimize downtime, and ensure resource utilization. However, existing approaches fall short due to complex data integration and failure to model long-range temporal dependencies. To address these challenges, the authors propose the Transformer-based Decision Support Network (T-DSN), which integrates multi-source data and temporal modeling into a unified framework for predictive maintenance, resource allocation, and production scheduling. Leveraging the Transformer architecture, T-DSN captures long-term dependencies in time-series sensor data and operational logs, enabling accurate predictions and real-time decision-making. Experiments on SECOM, C-MAPSS, CMHS, and Purdue Production Scheduling datasets show T-DSN outperforms baselines like XGBoost and LSTM by up to 2% in R2 predictive accuracy with reduced training times, supporting efficient, cost-effective manufacturing operations.
Wei Huang, A. Cheema· Journal of Organizational an...· 0 citations
Online tourist reviews, a major form of user-generated content (UGC), are often short and unstructured, complicating the identification of tourist experience dimensions and their relationships. This study presents an integrated framework combining Topic-RoBERTa, topic-level semantic network analysis, and Graph Attention Networks (GAT) to extract experience topics and model their semantic associations. The authors validated the framework on 186,429 reviews from the OD-TripM TripAdvisor review dataset released by the Data Science and Computational Intelligence (DaSCI) research group on GitHub and the Yelp Open Dataset, identifying 12 tourist experience dimensions and constructing a community-structured semantic network. The results show that the proposed method can extract interpretable topics, reveal fine-grained attention-weighted associations, and provide a more structured understanding of tourist experiences. The findings offer valuable insights for tourism management and service optimization.
Yawei Wu, Xin Liu, A. Cheema· Journal of Organizational an...· 0 citations
