Automatic guided vehicles (AGVs) have been successfully applied to cargo terminals to reduce operating costs and improve productivity. However, the focus was on container terminal operations. Ports with roll-on/roll-off (RORO) terminals still heavily depend on human resources for the loading/unloading processes. Work operations are affected by human errors and safety issues. In particular, terminals where vehicles cannot be stacked pressure workers to handle cargo more rapidly, which induces more errors. In this study, we propose automating RORO terminal operations by using AGVs. We assessed the impact of AGVs on the productivity, cost efficiency, and environment. A series of simulation models was developed on the basis of the current loading system at an actual port to test the impact of AGVs. Then, we developed a cost model to analyze the economic benefit of AGVs compared with the current loading system. The environmental benefits were also analyzed. Results revealed that a system using 29 AGVs matched the productivity of the current loading system, and using more AGVs increased the productivity. For a given productivity level, the total operating cost of the AGV system was three times less than that of the current system over a 15-year period. The AGV system also showed great potential for improving the environmental friendliness of terminal operations. This is the first study to propose automating RORO terminal operations to improve productivity and sustainability through AGV technology rather than human factors. AGVs are expected to become a good option in the future to address labor shortages and the “untact” era.
This paper aims to introduce the adaptation of automated guided vehicles (AGVs) in the car-loading process of Ro-Ro ships compared with the current loading process. This study analyzed the applicable scenarios for the AGVs’ adaptation in a Ro-Ro port, employing Arena simulation to compare the productivity of the loading processes. The results revealed that the adaptation of the AGVs in the car-loading process of the Ro-Ro ships improves productivity and solves several problems of the current loading process. With 21 or more AGVs, the entire processing time is similar to or less than the current loading process, whereas, after 40 AGVs, it stayed the same. Furthermore, as the number of AGVs increases, the transfer time decreases, but the queue becomes longer. Identifying the effect of the AGV adaptation, this study provides valuable insights for developing the various traffic situations in Ro-Ro port operations.
Local residents living adjacent to ports are directly affected by the fine dust generated from the port operations. There is a need to prepare detailed measures according to cargo type given the high correlation between the types of dust-producing cargo primarily managed at ports and local industries. This study attempts to establish the attributes of the cargo handled at ports and the relationship between supply chains built for local key industries and the air quality of the local community. It aims to ascertain which cargo needs managing preemptively at the local level, based on the major cargo types handled in a port. A correlation analysis and Granger causality test were performed to investigate the causality between the factor of cargo and fine dust concentrations. The results in this study indicate the necessity for intensive management of scrap metal cargo among the major cargo handled at the target port, which confirms the large effect of management on fine dust reduction, as well as on reduction efficiency. The results suggest requirements to expand the regulations on the emissions of supply chains by cargo type, not by industry type. Additionally, it is required to minimize the blind spots of management and form an eco-friendly supply chain by introducing green technology. The preparation of emission control measures is also necessary. The findings provide useful insights for the sustainable operations of the local supply chain around the target port and will help the strategic agenda for future improvement.
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