산업경영시스템학회지 (Journal of Korean Society of Industrial and Systems Engineering)

  • 제45권3호
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  • Pages.40-48
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  • 2022
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  • 2005-0461(pISSN)
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  • 2287-7975(eISSN)
한국산업경영시스템학회 (Society of Korea Industrial and System Engineering)
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자동화 물류시스템 내 차량 혼잡도를 고려한 무인운반차량의 동적 경로 결정 알고리즘

A Dynamic OHT Routing Algorithm in Automated Material Handling Systems

  • 강봉권 (부산대학교 산업공학과 산업데이터공학융합전공) ;
  • 강병민 (부산은행 디지털전략부) ;
  • 홍순도 (부산대학교 산업공학과)
  • Kang, Bonggwon (Major in Industrial Data Science & Engineering, Department of Industrial Engineering, Pusan National University) ;
  • Kang, Byeong Min (Digital Strategy Department, Busan Bank) ;
  • Hong, Soondo (Department of Industrial Engineering, Pusan National University)
  • 투고 : 2022.07.20
  • 심사 : 2022.09.15
  • 발행 : 2022.09.30
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초록

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.

키워드

과제정보

This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korean government(MSIT) (No. NRF-2020R1A2C2004320).

참고문헌

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