Enhancing bulk cargo unloading efficiency through AI: fuzzy logic application

Abstract

The efficiency of bulk cargo vessel unloading processes is a pivotal determinant of the overall economic and logistical performance of maritime transport systems. The optimization of these processes directly influences the throughput of shipping operations and the effective use of port infrastructure. Traditional unloading methodologies, which heavily rely on manual coordination and static operational protocols, often struggle to meet the dynamic demands of modern maritime trade. This study develops and assesses an artificial intelligence (AI) and fuzzy logic-based model to optimize bulk cargo unloading for Handymax Carriers, which are equipped with 5 hatches and 4 cranes. Traditional manual methods present inefficiencies that this technology aims to mitigate by improving crane allocation and adapting dynamically to operational conditions. The research demonstrates that the AI-enhanced approach significantly reduces unloading times and operational costs, showcasing substantial improvements over conventional strategies. Through a series of simulations, complemented by real-world application and testing, this research illustrates the capabilities and benefits of AI-human collaboration in maritime logistics. The findings suggest that the integration of AI can significantly boost operational efficiency, improve safety outcomes by reducing human error, and enhance the overall allocation of resources. This approach not only contributes to the technological advancement in the field of maritime logistics but also sets a foundation for future developments in intelligent transport systems where human expertise and AI solutions are intertwined for superior performance and decision-making.