Guest Editors
Prof. Chun-Ming Yang
Email: vicyang0706@gmail.com; 2020812@dgut.edu.cn
Affiliation: School of Economics and Management, Dongguan University of Technology, Dongguan 523808, China
Homepage:
Research Interests: Quality management, statistical process control, performance evaluation, multi-criteria decision-making, fuzzy decision-making
Prof. Kuen-Suan Chen
Email: kschen@ncut.edu.tw
Affiliation: Department of Industrial Engineering and Management, National Chin-Yi University of Technology, Taichung 411030, Taiwan
Homepage:
Research Interests: Statistical process control, quality management, process capability analysis, performance evaluation method, Six Sigma, fuzzy decision-making and service management
Prof. Dingxuan Huang
Email: huangdingxuan@cqut.edu.cn
Affiliation: School of Management, Chongqing University of Technology, Chongqing 401135, China
Homepage:
Research Interests: Quantum game, social network analysis, green building and design
Prof. Chun-Min Yu
Email: march@ncut.edu.tw
Affiliation: Department of Industrial Engineering and Management, National Chin-Yi University of Technology, Taichung 411030, Taiwan
Homepage:
Research Interests: Human resource management, life cycle assessments (LCA), quality management, service performance management, process capability analysis, Six sigma, fuzzy evaluation
Summary
With the advancement of technology, the methods for measuring and analyzing engineering data are constantly being innovated and improved. This enables the engineering data to deliver greater value, allowing analysis results to more closely reflect real-world situations. Emerging technologies such as Artificial Intelligence (AI), Big Data, Cloud Computing, and Machine Learning (ML) have a wide range of applications in the practice of design, control, planning, decision-making, and management activities in engineering manufacturing and services. These technologies can help expand, automate, and enhance the collection and processing of engineering data, and improve engineers' ability to formulate strategies and value-added analysis and insights, thereby enhancing engineering performance and quality. We expect the submitted papers to present solutions and examples based on mathematical modelling and optimization, algorithms for application domains, software implementation, and other formal approaches or technologies. We are inviting submissions to this Special Issue entitled “Advanced Approaches and Applications in Engineering”. Both original research and reviews will be considered. The following subtopics are the particular interests of this special issue, including but not limited to:
Artificial intelligence for data analytics in engineering;
Big data in engineering;
Machine learning/deep learning applications in engineering;
Statistics in engineering;
Performance evaluation in engineering;
System/process optimization in engineering;
Data analysis methods in engineering;
Uncertainty in engineering;
Decision-making in engineering.
Keywords
Artificial intelligence; Big data; Machine learning; Deep learning; Statistics; Performance evaluation; Optimization; Data analysis; Uncertainty; Decision-making
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