TY - JOUR
T1 - A two-phase decoding genetic algorithm for TFT-LCD array photolithography stage scheduling problem with constrained waiting time
AU - Hong, Tzu Yen
AU - Chien, Chen Fu
AU - Wang, Hung Kai
AU - Guo, Hong Zhi
N1 - Funding Information:
This research is supported by Ministry of Science and Technology, Taiwan (MOST 105-2811-E-007-050; MOST 106-2811-E-007-042; MOST 107-2634-F-007-002).
Publisher Copyright:
© 2018 Elsevier Ltd
PY - 2018/11
Y1 - 2018/11
N2 - The thin-film-transistor liquid crystal display (TFT-LCD) array process is usually the longest process in TFT-LCD production. During the TFT-LCD array process, the scheduling problem of the photolithography stage entails complicated constraints such as photo mask availability, available machines for jobs, and limited waiting time. This study proposes a two-phase decoding genetic algorithm (TDGA) to maximize utilization in the photolithography stage, which is usually the bottleneck. An empirical study was conducted at a leading TFT-LCD manufacturing company. To compare the performance of the TDGA with other metaheuristics, eight scenarios were simulated based on the empirical data. The experimental results show the practical viability of the proposed TDGA.
AB - The thin-film-transistor liquid crystal display (TFT-LCD) array process is usually the longest process in TFT-LCD production. During the TFT-LCD array process, the scheduling problem of the photolithography stage entails complicated constraints such as photo mask availability, available machines for jobs, and limited waiting time. This study proposes a two-phase decoding genetic algorithm (TDGA) to maximize utilization in the photolithography stage, which is usually the bottleneck. An empirical study was conducted at a leading TFT-LCD manufacturing company. To compare the performance of the TDGA with other metaheuristics, eight scenarios were simulated based on the empirical data. The experimental results show the practical viability of the proposed TDGA.
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U2 - 10.1016/j.cie.2018.08.024
DO - 10.1016/j.cie.2018.08.024
M3 - Article
AN - SCOPUS:85052846703
SN - 0360-8352
VL - 125
SP - 200
EP - 211
JO - Computers and Industrial Engineering
JF - Computers and Industrial Engineering
ER -