Recently, in the manufacturing industry, changes in various environmental conditions and constraints appear rapidly. At this time, a dispatching system that allocates work to resources at an appropriate time plays an important role in improving the speed or quality of production. In general, a rule-based static dispatching method has been widely used. However, this static approach to a dynamic production environment with uncertainty leads to several challenges, including decreased productivity, delayed delivery, and lower operating rates, etc. Therefore, a dynamic dispatching method is needed to address these challenges. This study aims to develop a reinforcement learning-based dynamic dispatching system, in which dispatching agents learn optimal dispatching rules for given environmental states. The state space represents various information such as WIP(work-in-process) and inventory levels, order status, machine status, and process status. A dispatching agent selects an optimal dispatching rule that considers multiple objectives of minimizing total tardiness and minimizing the number of setups at the same time. In particular, this study targets a multi-area manufacturing system consisting of a flow-shop area and a cellular-shop area. Thus, in addition to the dispatching agent that manages inputs to the flow-shop, a dispatching agent that manages transfers from the flow-shop to the cellular-shop is also developed. These two agents interact closely with each other. In this study, an agent-based dispatching system is developed and the performance is verified by comparing the system proposed in this study with the existing static dispatching method.

1. 서 론
2. 기존 연구 고찰
    2.1 강화학습 기반 디스패칭 연구 동향
    2.2 선행연구
3. 문제 정의
    3.1 다중구역 제조시스템에서의 디스패칭
    3.2 에이전트 기반 디스패칭 시스템
4. 강화학습 모델
    4.1 투입 시점에서의 MDP
    4.2 구역 간 이송 시점에서의 MDP
5. 시뮬레이션 실험
    5.1 시뮬레이션 테스트베드
    5.2 학습
    5.3 검증
6. 결 론
References

• Minjung Kim(Department of Industrial and Management Engineering, Hanbat National University) | 김민정 (국립한밭대학교 산업경영공학과)
• Moonsoo Shin(Department of Industrial and Management Engineering, Hanbat National University) | 신문수 (국립한밭대학교 산업경영공학과) Corresponding author