다중구역 제조시스템을 위한 에이전트 기반 디스패칭 시스템
Agent-based Dispatching System for a Multi-area Manufacturing System
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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.
Abstract
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.
참고문헌 (16)
- Aydin, M. E., "Dynamic job-shop scheduling using reinforcement learning agents", Robotics and Autonomous Systems, 33(2-3), pp.169~178 (2000)
- Cho, Y. I., "Development of the Reinforcement Learning-based Adaptive Scheduling Algorithm for Panel Block Shop", Korean Journal of Computational Design and Engineering, 26(2), pp.81~92 (2021)
- "Hanbat National University"[URL]
- Kang, B. W., "A Dynamic OHT Routing Algorithm in Automated Material Handling Systems", Journal of Korean Society of Industrial and Systems Engineering, 45(3), pp.40~48 (2022)
- Kim, M. J., "A Reinforcement Learning Model for Dispatching System through Agent-based Simulation", Journal of Korean Society of Industrial and Systems Engineering, 47(2), pp.116~123 (2024)
- Nam, S. H., "Reinforcement Learning for Minimizing Tardiness and Set-Up Change in Parallel Machine Scheduling Problems for Profile Shops in Shipyard", Journal of the Society of Naval Architects of Korea, 60(3), pp.202~211 (2023)
- The 4th Industrial Revolution Era:Characteristics and Policy Recommendations for Seoul Manufacturing, "Oh, E. J.", 1~23, pp.No. 238 (2017)
- Park, H.S., "A Study on Big Data-driven SMART Production Scheduling for Manufacturing System", Master, pp.2019
- Shen, W., "Applications of agent-based systems in intelligent manufacturing : An updated review", Advanced Engineering Informatics, 20(4), pp.415~431 (2006)
- Stricker, N., "Reinforcement learning for adaptive order dispatching in the semiconductor industry", CIRP Annals, 67(1), pp.511~514 (2018)
- "The AnyLogic Company"[URL]
- Wang, Y. C., "Application of reinforcement learning for agent-based production scheduling", Engineering applications of artificial intelligence, 18(1), pp.73~82 (2005)
- Waschneck, B., "Deep reinforcement learning for semiconductor production scheduling", Proceedings of the 29th Annual SEMI Advanced Semiconductor Manufacturing Conference (ASMC), pp.301~306 (2018)
- Yoo, W. S., "Real-Time Scheduling Scheme based on Reinforcement Learning Considering Minimizing Setup Cost", The Journal of Society for e-Business Studies, 25(2), pp.15~27 (2020)
- Zhang, Z., "Flow Shop Scheduling with Reinforcement Learning", Asia-Pacific Journal of Operational Research, 30(5) (2013)