Abstract
Recently, automated material handling systems (AMHSs) in semiconductor fabrication plants (FABs) in South Korea have become a new and major bottleneck. This is mainly because the number of long-distance transportation requests has increased as the FAB area has widened. This paper presents a deep-learning-based adaptive method for the storage-allocation problem to improve the AMHS throughput capacity.
The AMHS in this research consists of overhead hoist transfer transports (OHTs), a unified rail for the OHTs, etc. The main problem involves scheduling (or designating) an intermediate buffer, e.g., a stocker or a side track buffer, for a single lot. Thus far, a static optimization approach has been widely applied to the problem. This research shows that a learning-based adaptive storage-allocation strategy can increase the AMHS capacity in terms of throughput. The deep-learning model considers various production conditions, including processing time, transportation time, and the distribution of works in process (WIP).
This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (NRF-2017R1D1A3B03028784).
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Kim, H., Lim, DE. (2018). Deep-Learning-Based Storage-Allocation Approach to Improve the AMHS Throughput Capacity in a Semiconductor Fabrication Facility. In: Li, L., Hasegawa, K., Tanaka, S. (eds) Methods and Applications for Modeling and Simulation of Complex Systems. AsiaSim 2018. Communications in Computer and Information Science, vol 946. Springer, Singapore. https://doi.org/10.1007/978-981-13-2853-4_18
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