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Manufacturing Process Optimization in the Process Industry

Shilin Liu & Hanlie Cheng

International Journal of Information Technology and Web Engineering2024https://doi.org/10.4018/ijitwe.338998article
ABDC C
Weight
0.76

What the paper says

This paper introduces a technology, a data-driven optimization model of manufacturing service in intelligent manufacturing process using deep learning algorithm and resource agent (DDR), and a data-driven resource agent that represents available manufacturing resources. Asset agent is an intelligent module of entity production unit, which has powerful functions of data processing and service management. This paper includes the method of designing expert-based processes, the current process realization model, and the key performance indicators (KPI) used to evaluate the optimization work. The model aims to maximize efficiency, reduce the cost of manufacturing resources, improve the production and maintenance efficiency of network resources, and improve the manufacturing service level. Finally, the efficiency and technical feasibility of the model are evaluated through a typical example of industrial product production process.

26 citations

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https://doi.org/https://doi.org/10.4018/ijitwe.338998

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@article{shilin2024,
  title        = {{Manufacturing Process Optimization in the Process Industry}},
  author       = {Shilin Liu & Hanlie Cheng},
  journal      = {International Journal of Information Technology and Web Engineering},
  year         = {2024},
  doi          = {https://doi.org/https://doi.org/10.4018/ijitwe.338998},
}

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Manufacturing Process Optimization in the Process Industry

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Evidence weight

0.76

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.97 × 0.4 = 0.39
M · momentum1.00 × 0.15 = 0.15
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.