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.
Spare part management is very important to products that have large number of parts and long lifecycle such as automobile and aircraft. Supply chain must support immediate procurement for repair. However, it is not easy to handle spare parts efficiently due to huge stock keeping units. Qualified forecasting is the basis for the supply chain to achieve the goal. In this paper, we propose an agent based modeling approach that can deal with various factors simultaneously without mathematical modeling. Simulation results show that the proposed method is reasonable to describe demand generation process, and consequently, to forecast demand of spare parts in long-term perspective.
Land suitability assessment assesses development, farming, and conservation suitability, considering land's soil, location, and possibility for use. It also implement segmentation of management regions into production, conservation, and plan management area. It is evaluated as a very significant system in establishing a land use system of sustainable development and development after planning in the aspect that it can establish proper land use plan. This study developed a recommendation model for development in agent-based model that interacts with surrounding lands. It also tried to summarize the area characteristic analysis and the results of land suitability evaluation, targeting three ri's in Yesan-Gun, and analyze the model's applicability by selection results. In order to recommend area for development that considers the use of the surrounding lands, it calculated development possibility indices that considered the ratings of all the lands in the target areas for each parcel and simulated the model. As a result, selected three areas in target region were suitable areas for development in land suitability assessment. In detail, ratings of the recommended parcels were 3, 4, and 5 ratings. As a result of examining the land status, it showed that all the three areas were plan management areas, thus easy for development. It is judged that the model for recommending area for development suggested in this study can be used as important basic data for setting the direction for development when establishing a regional planning.