International Journal of
COMPUTER INTEGRATED MANUFACTURING
Special Issue: Data-Driven Modeling and Analytics for Optimization of Complex Manufacturing Systems
Wei Qin (Managerial Guest Editor), Department of Industrial Engineering & Management, Shanghai Jiao Tong University, China - Email:
Yingfeng Zhang, College of Mechanical and Electrical Engineering, Northwestern Polytechnical University, China
Ting Qu, School of Electrical and Information Engineering, Jinan University, China
Xinyu Li, School of Mechanical Science and Engineering, Huazhong University of Science and Technology, China
Deadline of Manuscript Submission: 1st September 2020
Tentative Publication Date: Summer 2021
To prepare the manuscripts, authors should follow the “Instructions for authors” presented at the journal homepage.
To make your submission, please IJCIM's electronic submission system: https://mc.manuscriptcentral.com/tcim.
Full papers should follow the IJCIM guidelines and clearly indicate“Special issue on Data-Driven Modeling and Analytics for Optimization of Complex Manufacturing Systems” in the cover letter. For further enquiries, please contact the managerial guest editor.
About the special issue
As the core issue for improving manufacturing system performance (such as product quality, production efficiency and cost, etc.), modeling, analysis and optimization of manufacturing systems have always been studied towards smart manufacturing. However, due to the large-scale and dynamic characteristics of complex manufacturing systems, the causal relationship between system performance and manufacturing process parameters is difficult to determine. Therefore, traditional models and algorithms are now facing some challenges such as the “Curse of Dimensionality”, high computational complexity and so on.
With the rapid development of information technology, massive production data is captured, practitioners and academics are paying more and more attention to the huge value hidden behind the data. Data-driven technology can effectively help for revealing the inherent laws of complex manufacturing processes, transforming data into production and operation knowledge, optimizing production processes, improving product quality and production efficiency, as well as enhancing product lifecycle management level.
Data-driven modeling and analysis has become one of the most promising methods for optimization of complex systems, and has made important breakthroughs in many research areas (Runge et al., 2019; Severson et al., 2019). In biology science, data-driven modeling and analysis has been used to quantitatively identify direct dependencies between genes, reconstruct gene regulatory network and causal relations (Zhao et al., 2016), and identify cell behaviors affecting the observed aggregation dynamics without full knowledge of the underlying biological mechanisms (Cotter et al., 2017). In medical science, data-driven modeling and analysis can not only systematically explore the molecular complexity of specific diseases, but also identify disease modules and pathways, as well as the molecular relationships between distinct phenotypes (Cheng et al., 2019). These successful cases provide a new way of thinking for the modeling, analysis and optimization of complex manufacturing systems in the industrial field. How to use the big data to establish an effective model describing the complex manufacturing system? How to analyze the data-driven model to reveal the law of system operation? How to dynamically regulate the data-driven model to optimize the system performance? This special issue provides an opportunity for academia and practitioners to share state-of-the-art research and cases in the field of modeling, optimization and control in manufacturing systems. Original works are invited for consideration for publication.
Topics of interest
Original, high quality theoretical and empirical research papers are invited for submissions in this special issue. Typical topics include, but not limited to, following topics:
State-of-the-art and future perspectives of data-driven technology, especially in the industrial and manufacturing fields
Data-driven modeling and simulation of manufacturing systems
Analysis of big data in Manufacturing system
Data-driven analysis of manufacturing system features and functional performance
Data-driven model based optimization of manufacturing system
Dynamic control of the manufacturing system model
All submissions will be judged for their appropriateness to the journal’s remit and the novelty of their theoretical and practical research contributions. While quantitative research is preferred, relevant qualitative research studies as well as case studies are also welcomed.
Cheng, F., Kovács, I. A. and Barabási, A. L. (2019). " Network-based prediction of drug combinations." Nature communications 10(1): 1197.
Cotter, C. R., Schüttler, H. B., Igoshin, O. A. and Shimkets, L. J. (2017). " Data-driven modeling reveals cell behaviors controlling self-organization during Myxococcus xanthus development." Proceedings of the National Academy of Sciences 114(23): 4592-4601.
Runge, J., Bathiany, S., Bollt, E., et al. (2019). " Inferring causation from time series in Earth system sciences." Nature communications 10(1): 2553.
Severson, K. A., Attia, P. M., Jin, N., et al. (2019). " Data-driven prediction of battery cycle life before capacity degradation." Nature Energy 4(5): 383.
Zhao, J., Zhou, Y., Zhang, X. and Chen, L. (2016). " Part mutual information for quantifying direct associations in networks." Proceedings of the National Academy of Sciences 113(18): 5130-5135.