Artificial Intelligence Driven Smart Manufacturing and Service Operations Management
With the continuous popularization of artificial intelligence, big data, cloud computing and other new generation information technologies, the fields of smart manufacturing and service operations management are facing great changes in transforming to intelligence operating system; also, they have brought infinite possibilities for the industry, society, and even human development.
Against a background of big data, artificial intelligence has great potential in areas closely related to human life. It can develop modelling techniques to realize the collaboration of human and machine, improve the performance of complex system control for variable operations environment, and contribute to the optimal allocation of public service resources. The development of corresponding technologies such as smart production, intelligent healthcare, online education, smart transportation, signal processing, voice recognition, and brain-computer interface can help increase the efficiency of social operation. In addition, it can also optimize the use of resources through intelligent devices for health diagnosis, system controller design, energy supply structure optimization, consumption patterns and demand forecasting, etc. Despite the large amount of work in this emerging research area, there are still many open technical challenges, such as the acquisition and utilization of multimodal information, accurate predictive models, the integration of AI into traditional optimization methods and system optimization control. Thus, advanced theoretical and methodological support is still needed to address issues related to this research area.
AI technologies are gradually penetrating, influencing and changing the research paradigm in the fields related to information systems and operations management. To further meet the needs of industry change and economic development, this special issue invites papers on the intersection of artificial intelligence and management science technologies, and aims to promote their development of the cutting-edge effective applications in smart manufacturing, logistics, service operations management, system control, communications and automation, and other related promising directions.
Submissions to this special issue can be theoretical, methodological, computational or application-oriented which share technical and scientific findings and visions in the areas of smart manufacturing and service operations.
Topics of the special issue interests and focuses include, but not limited to
- New theories and methods of smart manufacturing and service operations management under the new generation of AI technology;
- Model exploration and technical innovation of industrial interconnection and smart manufacturing；
- Theories and designs of data-driven control to achieve higher stabilization, optimality, and robustness within the industrial environment;
- Information analysis assisted computer control systems, factory communications and automation;
- Novel decision architectures and paradigms for AI-based manufacturing and service operations management;
- Combination of AI technologies, such as machine learning, with classical operations research techniques for smart manufacturing and service operations management;
- Prediction technique with big data in smart manufacturing and services;
- Collaborative operation optimization of smart manufacturing and services;
- Data-driven optimization of complex systems in practical fields, such as intelligent manufacturing, supply chain management, healthcare management, sharing economy, robotics and computer control systems, etc.;
- Comparison between classical optimization methods and AI-driven approaches for operations management modeling and solution.
Manuscript Submission Deadline: October 30, 2022
Notification of Paper Decision: December 31, 2022
Revised Paper Submission Deadline: March 31, 2023
Final Paper Submission Deadline: June 30, 2023
Publication Date: 2023
Prof. Dujuan Wang, Business School, Sichuan University, China, [email protected]
Prof. Jiafu Tang, School of Management Science and Engineering, Dongbei University of Finance and Economics, China, [email protected]
Prof. Youhua Chen, Department of Management Sciences, City University of Hong Kong, Kowloon, Hong Kong SAR, China, [email protected]
Prof. Yunqiang Yin, School of Economics and Management, University of Electronic Science and Technology of China, China, [email protected]
Prof. Yaochu Jin, Faculty of Technology, Bielefeld University, Germany, [email protected]