Machine Learning driven Optimization of Overhead Hoist Transport System Control in Semiconductor Industry
Research output: Contribution to book/conference proceedings/anthology/report › Conference contribution › Contributed › peer-review
Contributors
Abstract
Automated material handling systems (AMHS) are essential for industrial semiconductor production in modern front-end facilities. The control of these systems has a significant impact on ensuring a reliable supply of production resources. Allocating transportation tasks to vehicles in real time is of great importance here, as it represents a computational challenge and has a major impact on the performance of the transportation system (see Wu et al., 2019). Achieving the best possible operation is the subject of numerous research activities (see De Ryck et al., 2020).
Machine learning approaches enable new ways of developing control strategies to achieve higher system performance (see Bai et al., 2023). Our paper provides two examples of how machine learning can be applied to improve task assignment for empty vehicles.
Machine learning approaches enable new ways of developing control strategies to achieve higher system performance (see Bai et al., 2023). Our paper provides two examples of how machine learning can be applied to improve task assignment for empty vehicles.
Details
Original language | English |
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Title of host publication | 22nd European Advanced Process Control and Manufacturing Conference (apc|m) |
Pages | 1-7 |
Number of pages | 7 |
Publication status | Published - 16 Apr 2024 |
Peer-reviewed | Yes |
Conference
Title | 22nd European Advanced Process Control and Manufacturing Conference |
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Abbreviated title | apc|m 2024 |
Conference number | 22 |
Duration | 16 - 18 April 2024 |
Website | |
Degree of recognition | International event |
Location | CinemaxX Hamburg |
City | Hamburg |
Country | Germany |
External IDs
ORCID | /0000-0002-1012-8337/work/161407708 |
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ORCID | /0000-0002-1484-7187/work/161408880 |