An automated material handling system (AMHS) has been emerging as an important factor in the semiconductor wafer manufacturing industry. In general, an automated guided vehicle (AGV) in the Fab’s AMHS travels hundreds of miles on guided paths to transport a lot through hundreds of operations. The AMHS aims to transfer wafers while ensuring a short delivery time and high operational reliability. Many linear and analytic approaches have evaluated and improved the performance of the AMHS under a deterministic environment. However, the analytic approaches cannot consider a non-linear, non-convex, and black-box performance measurement of the AMHS owing to the AMHS’s complexity and uncertainty. Unexpected vehicle congestion increases the delivery time and deteriorates the Fab’s production efficiency. In this study, we propose a Q-Learning based dynamic routing algorithm considering vehicle congestion to reduce the delivery time. The proposed algorithm captures time-variant vehicle traffic and decreases vehicle congestion. Through simulation experiments, we confirm that the proposed algorithm finds an efficient path for the vehicles compared to benchmark algorithms with a reduced mean and decreased standard deviation of the delivery time in the Fab’s AMHS.

1. 서 론
2. 문헌연구
3. 반도체 자동화 물류시스템
    3.1 Fab AMHS 레이아웃
    3.2 OHT 반송작업 및 정체현상
    3.3 Fab AMHS 시뮬레이션 모델링
4. 정체경로 우회 위한 동적 경로 결정
    4.1 Fab AMHS 내 OHT 경로 결정 문제
    4.2 Q-Learning 기반 동적 경로 결정 알고리즘
    4.3 OHT간 정체패턴을 고려한 상태모델링
5. 시뮬레이션 실험
6. 결 론
Acknowledgement
References

• Bonggwon Kang(부산대학교 산업공학과 산업데이터공학융합전공) | 강봉권
• Byeong Min Kang(부산은행 디지털전략부) | 강병민
• Soondo Hong(부산대학교 산업공학과) | 홍순도 Corresponding author