Machine Learning driven Optimization of Overhead Hoist Transport System Control in Semiconductor Industry

Research output: Contribution to book/conference proceedings/anthology/reportConference contributionContributedpeer-review



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.


Original languageEnglish
Title of host publication22nd European Advanced Process Control and Manufacturing Conference (apc|m)
Number of pages7
Publication statusPublished - 16 Apr 2024


Title22nd European Advanced Process Control and Manufacturing Conference
Abbreviated titleapc|m 2024
Conference number22
Duration16 - 18 April 2024
Degree of recognitionInternational event
LocationCinemaxX Hamburg

External IDs

ORCID /0000-0002-1012-8337/work/161407708
ORCID /0000-0002-1484-7187/work/161408880