Logistical Assessment of Deep-Sea Polymetallic Nodules Transport from an Offshore to an Onshore Location Using a Multiobjective Optimization Approach

Abstract

The increasing growth in the global population has led to a substantial demand for low-carbon energy infrastructure, metals, and minerals. This has put more pressure on land-based deposits, which have been unsustainably exploited over the years. As a result, attention has shifted towards exploring minerals in sea-based environments. Currently, industry and researchers have identified potentially commercially viable locations for the exploration of these nodules. However, significant knowledge gaps remain in the sustainable, efficient, and effective recovery and transportation of the nodules to onshore locations. To address these gaps, the study develops a logistics and cost model embedded in a multiobjective optimization (MOO) approach. This model considers several parameters, such as the production targets, port distance and location, storage capacity, vessel characteristics, transportation options, and cost inputs. By incorporating these parameters, the study analyzes different combinations of vessel classes and onshore locations and provides insights into optimizing offshore–onshore logistics and transportation options. The findings reveal that small and medium-sized vessels require lower storage capacity because they can complete more trips. Furthermore, the analysis reveals the cost of deploying additional vessels outweighs the benefits of reduced storage space for long-distance transport; therefore, smaller and medium-sized vessels are more suitable for locations closer to the offshore production site. Additionally, proximity to the onshore location is important, as it reduces transport costs and simplifies logistics operations. Subsequently, there is a need to have a reasonable buffer rate as this reduces the impact of potential disruptions during transport. From a managerial viewpoint, the study highlights the need to carefully consider vessel types based on transport requirements and journey characteristics. The analysis further identifies the benefits of having an onshore location close to the offshore production site. This will lead to optimized transport and logistics operations. Based on this, the study contributes to the body of knowledge in offshore logistics by developing a multiobjective optimization model for offshore–onshore transport logistics and cost analysis. This model provides a practical tool for informed decision-making and provides insight into vessel size and location considerations. Finally, the study establishes how simultaneous consideration of multiple factors in transport operations can lead to optimized and informed decision-making.

The Wide-Area Coverage Path Planning Strategy for Deep-Sea Mining Vehicle Cluster Based on Deep Reinforcement Learning

“…The main components of a deep-sea mining system include seabed mining vehicles [18][19][20], transportation hoses, relay stations, lifting hard pipes [21], and surface mining vessels [22][23][24]. The complete system structure is shown in Figure 1.…”