In the manufacturing industry, dispatching systems play a crucial role in enhancing production efficiency and optimizing production volume. However, in dynamic production environments, conventional static dispatching methods struggle to adapt to various environmental conditions and constraints, leading to problems such as reduced production volume, delays, and resource wastage. Therefore, there is a need for dynamic dispatching methods that can quickly adapt to changes in the environment. In this study, we aim to develop an agent-based model that considers dynamic situations through interaction between agents. Additionally, we intend to utilize the Q-learning algorithm, which possesses the characteristics of temporal difference (TD) learning, to automatically update and adapt to dynamic situations. This means that Q-learning can effectively consider dynamic environments by sensitively responding to changes in the state space and selecting optimal dispatching rules accordingly. The state space includes information such as inventory and work-in-process levels, order fulfilment status, and machine status, which are used to select the optimal dispatching rules. Furthermore, we aim to minimize total tardiness and the number of setup changes using reinforcement learning. Finally, we will develop a dynamic dispatching system using Q-learning and compare its performance with conventional static dispatching methods.

1. 서 론
2. 관련 연구
    2.1 강화학습 기반 디스패칭
    2.2 강화학습
    2.3 Q-learning 알고리즘
3. 강화학습 모델
    3.1 문제 정의
    3.2 Markov Decision Process
4. 에이전트 기반 디스패칭 시스템
5. 시뮬레이션 실험
    5.1 시뮬레이션 테스트베드
    5.2 시뮬레이션 시나리오
    5.3 실험 결과
6. 결 론
Acknowledgement
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