시스템 특성함수 기반 평균보상 TD(λ) 학습을 통한 유한용량 Fab 스케줄링 근사화

• 영문명: Capacitated Fab Scheduling Approximation using Average Reward TD(λ) Learning based on System Feature Functions
• 발행기관: 한국산업경영시스템학회
• 저자명: 최진영(Jin Young Choi)
• 간행물 정보: 『산업경영시스템학회지』제34권 제4호, 189~196쪽, 전체 8쪽
• 주제분류: 경제경영 > 경영학
• 파일형태: PDF
• 발행일자: 2011.12.31

영문 초록

In this paper, we propose a logical control-based actor-critic algorithm as an efficient approach for the approximation of the capacitated fab scheduling problem. We apply the average reward temporal-difference learning method for estimating the relative value functions of system states, while avoiding deadlock situation by Banker’s algorithm. We consider the Intel mini-fab re-entrant line for the evaluation of the suggested algorithm and perform a numerical experiment by generating some sample system configurations randomly. We show that the suggested method has a prominent performance compared to other well-known heuristics.

목차

1. 서론

2. 문제 정의 및 관련 연구 동향

3. NDP 모델링 및 근사화 알고리즘

4. 실험 설계 및 결과

5. 결론

참고문헌

키워드

Fab Scheduling Problem Actor-critic Temporal-difference Average Reward Banker’s Algorithm Feature Functions