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An MDP-based lifter assignment algorithm for inter-floor transportation in semiconductor fabrication

Authors
Shin, KyohongJang, HoonKim, Haejoong
Issue Date
Apr-2022
Publisher
SPRINGER LONDON LTD
Keywords
Automated material handling system; Lifter; Markov decision process; Manufacturing; Semiconductor
Citation
INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY, v.119, no.9-10, pp.6583 - 6598
Indexed
SCIE
SCOPUS
Journal Title
INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY
Volume
119
Number
9-10
Start Page
6583
End Page
6598
URI
https://scholar.korea.ac.kr/handle/2021.sw.korea/140258
DOI
10.1007/s00170-021-08387-3
ISSN
0268-3768
Abstract
As semiconductor device geometries continue to shrink, the semiconductor manufacturing process becomes increasingly complex. This usually results in unbalanced utilization of machines and decreases overall productivity. One way to resolve such a problem is to share the manufacturing resources between different lines divided by floors. To this end, designing an efficient lifter assignment method to more efficiently manage transfer requests (TRs) of wafer lots to different floors is required. Motivated by this, our study addresses the assignment of lifters for delivering wafer lots to different floors. In contrast to previous studies that consider only the current state of the system, our approach considers both the current and possible future states of the system in a probabilistic manner in the Markov decision process. To overcome the curse of dimensionality of the original problem, we design an efficient algorithm using clustering, partitioning, and tournament methods. Experiments based on historical data confirm the effectiveness of the proposed algorithm in reducing transportation times and delivery delays compared to the benchmark rules in practice and the method in the state-of-the-art. Sensitivity analysis demonstrates the robustness of the proposed model as the number of TRs increased. The proposed approach is expected to yield significant economic savings in both operating costs and labour.
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