Process bottlenecks identification and its root cause analysis using fusion-based clustering and knowledge graph

Tang, Junya; Liu, Ying; Lin, Kwei-Jay; Li, Li

doi:10.1016/j.aei.2022.101862

Cited by 16 publications

(4 citation statements)

References 46 publications

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“…Reference [25] presented a KG-based reasoning question-answering system, achieving state-of-the-art results. Reference [26] introduced a KG clustering methodology based on information fusion, and [27] proposed a KG toolkit for data science applications. Reference [28] conducted a detailed survey of KG representation, highlighting hotspots, contributing countries, and top literature.…”

Section: ) Knowledge Graph and Static Graph Explainabilitymentioning

confidence: 99%

“…Another avenue worth exploring is the application of Graph Neural Networks (GNNs) within our framework. Existing literature [2], [5], [6], [22], [26] has explored GNNs for explainability, but their application to KGs and utilization of homophily and heterophily-based metrics for reasoning remain unexplored areas. KG reasoning and embedding itself is a compelling research topic.…”

Section: Limitations and Future Workmentioning

confidence: 99%

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A Network Analysis-Driven Framework for Factual Explainability of Knowledge Graphs

Munir,

Malick,

Ferretti

2024

IEEE Access

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Knowledge Graphs are widely used to represent knowledge structures in complex domains. In most real-world scenarios, these knowledge structures are dynamic. As a result, measures must be developed to assess the robustness and usability of Knowledge Graphs in temporal settings. Additionally, the explainability of inherent knowledge constituents is crucial for the desired attention of Knowledge Graphs, particularly in temporal settings. In this paper, we developed a framework to understand the robustness of factual explainability of Knowledge Graphs. The method is further verified by using meso-level attributes of the knowledge graph. The complex network analysis along with the community structures are co-evaluated through homophilic and heterophilic properties within the graph to validate the robustness of the factual interpretations. The analysis reveals that symbolic representation could be used as a reasonable metric for extracting link-based communities.INDEX TERMS Knowledge graph analysis, complex network analysis, homophily and heterophily analysis, factual reasoning and explainability, interpretability.

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Section: ) Knowledge Graph and Static Graph Explainabilitymentioning

confidence: 99%

Section: Limitations and Future Workmentioning

confidence: 99%

A Network Analysis-Driven Framework for Factual Explainability of Knowledge Graphs

Munir,

Malick,

Ferretti

2024

IEEE Access

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“…For example, 107. Tang et al [107] proposed seven rules for assigning points for the research on concentrated solar power generation. Qualitative analysis and quantitative analysis are equally important.…”

Section: Two Analysis Paths Of S-lcamentioning

confidence: 99%

Assessing the Life Cycle Sustainability of Solar Energy Production Systems: A Toolkit Review in the Context of Ensuring Environmental Performance Improvements

Tan,

Wu,

Zhang

et al. 2023

Sustainability

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In order to pursue clean, low-carbon, safe, and efficient energy utilization and accelerate the development of new energy, sustainability is the necessary research. In recent decades, solar power generation has rapidly formed and been widely applied. Sustainability analysis is a key aspect that directly affects the construction of solar power projects when conducting solar power generation construction. This paper attempts to start with life cycle sustainability assessment (LCSA) and study the status quo of its three pillars (These three pillars include life cycle assessment, life cycle cost assessment, and social life cycle assessment) in the field of solar power generation. At the same time, the externality indicators are considered between pillars. In addition, the application of uncertainty analysis was studied during the analysis process to address the impact of various random factors. The conclusion shows that further research is needed to support this transition by integrating life cycle assessment, life cycle cost, and social life cycle assessment into LCSA for the evaluation. To improve the effectiveness of future research, studies should focus on fulfilling major data gaps in the literature such as the lack of detailed documentation for specific materials and background process choices in life cycle assessment databases. The development of solar power generation can be an important alternative in efforts to decrease climate change impacts and pursue cleaner energy sources in countries where solar energy is more easily available by integrating life cycle assessment (LCA), life cycle cost (LCC), and social life cycle assessment (SLCA) into LCSA. The sustainable development of the solar power generation industry in terms of multiple aspects is supported.

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“…One of the primary issues within production systems is the identification and resolution of bottlenecks. Focusing on improving bottleneck performance is pivotal for optimizing production systems, and accurately pinpointing production bottlenecks within the workshop is crucial for effective production management [4][5][6][7] .…”

Section: Introductionmentioning

confidence: 99%

Bottleneck identification and transfer prediction for automated production lines based on FNN

Si,

Zhou,

Guo

et al. 2024

J. Phys.: Conf. Ser.

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In a re-entrant production system, the throughput of the whole system depends on the capacity of the bottleneck machine. In this study, a new definition of bottleneck is proposed for a precision forging blade shop. The reinforcement learning algorithm is used to optimize the production scheduling to determine the most suitable scheduling scheme, which lays the foundation for bottleneck identification. Subsequently, the bottleneck identification index system was established according to the optimization objective, and the bottleneck identification problem was transformed into a multi-attribute decision-making problem. Finally, a fuzzy neural network is used for training, and the basic scheduling examples of each flow shop are utilized for bottleneck identification and prediction to verify their effectiveness.

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Process bottlenecks identification and its root cause analysis using fusion-based clustering and knowledge graph

Cited by 16 publications

References 46 publications

A Network Analysis-Driven Framework for Factual Explainability of Knowledge Graphs

A Network Analysis-Driven Framework for Factual Explainability of Knowledge Graphs

Assessing the Life Cycle Sustainability of Solar Energy Production Systems: A Toolkit Review in the Context of Ensuring Environmental Performance Improvements

Bottleneck identification and transfer prediction for automated production lines based on FNN

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