부산대학교 도서관

Global logistics systems are in an unprecedented crisis from the pandemic, workforce disruptions, supply shortages, and demand surges. Shortages of goods and services, surges of demand, and an evolving workforce call for methods that address system resilience. Maritime ports in particular are vulnerable to these disruptions. The container stacking process is especially vulnerable, as it is a bottleneck in container management. This paper presents a simulation and reinforcement learning methodology for managing container stacking blocks. The container stacking problem is known to be difficult or impossible to optimize, with most ports using black box heuristic models. A simulation and reinforcement learning approach addresses these challenges. Simulation is an effective tool for and testing different inputs, parameter changes, noise, and changes to systems due to disruptive scenarios. Reinforcement learning is especially helpful for contexts in which traditional optimization is cost and computationally prohibitive, or where data is difficult to collect and analyze. This paper applies reinforcement learning with a mathematical simulation of the container stacking problem, achieving similar results to maritime ports. The results can be used to assess performance under disruptive scenarios, as well as to test new container storage configurations.