Optimization of Restricted Container Relocation Using the Monte Carlo Tree Search Method

Abstract This article explores how to improve operational performance in maritime ports by managing the flow of goods effectively. This study proposes an innovative approach based on Reinforcement Learning (RL), specifically the Monte Carlo Tree Search (MCTS) method, to address the restricted container relocation problem (RCRP). This method aims to determine an optimal sequence for container retrieval based on their respective priorities, in order to minimize the number of necessary relocations. By employing precise actions and a defined reward function, MCTS is guided towards the best possible solution. The efficiency and relevance of this method are demonstrated through various solved scenarios and compared to a literature-based approach using genetic algorithms. The results show that the MCTS approach is effective in addressing the complex challenges of goods flow management in maritime ports.