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.

Gradually recent automatic systems require how to manipulate all shared internal resources of system including components manufacturing equipments. Especially practical operation schedulings of manufacturing are gradually inevitable procedures in dynamic industrial environments. We suggest and evaluate a dynamic scheduling rule of machine and material handling system for on-line operation in job shop type FMS. Because alternating status should be included in operation scheduling procedures effectively and without delay in dynamic industrial environments, the mutual interaction between the machine operation scheduling and AGV dispatching rule was also studied to be based on simulation. This study compared the performance of the evaluation which was obtained from Dynamic Scheduling of FMS, and developed the Priority Rule Matrix for switching mechanism in dynamic environment by using those results.