Dual strategies-based resilience enhancement in a bulk cargo port under dynamic machinery failure scenarios with reinforcement learning

Abstract

The operational efficiency and resilience of bulk cargo terminals are crucial. They are not only a solid support for core areas such as energy supply, food security, and manufacturing, but also shoulder the heavy responsibility of transporting key materials such as coal, crude oil, grains, and minerals, ensuring the smooth and unobstructed operation of the global economy. This study focuses on how to enhance the resilience of bulk cargo terminals in the face of mechanical failures. Given the limitations of traditional coping strategies and mathematical modeling in dealing with dynamic uncertainties in port operations, we innovatively propose a dual strategy approach. This method cleverly combines dynamic berth replanning with mechanical equipment maintenance, and utilizes cutting-edge techniques of reinforcement learning (RL) for optimization. By developing an intelligent decision-making framework that can intelligently integrate the above strategies, providing a breakthrough solution for reducing downtime and enhancing terminal resilience. Through a case study of a specific bulk cargo port, we have verified the effectiveness of this strategy and revealed its enormous potential in significantly improving the operational efficiency of bulk cargo terminals. This study not only brings new dimensions of thinking to the field of port operations and logistics, but also emphasizes the crucial role of RL in developing flexible and resilient operational strategies to address the complex and ever-changing challenges of modern trade environments.