In a bid to streamline operations and cut down on energy use, researchers from the Institute of Logistics Science and Engineering at Shanghai Maritime University have developed a novel approach to managing sea-rail intermodal container terminals. The study, led by YANG Jiazhu, YU Fang, and YANG Yongsheng, focuses on optimizing the layout and scheduling of shared yards between railway and terminal operation areas, aiming to minimize operation completion time and energy consumption.
The team’s work, published in ‘Jisuanji gongcheng’ (translated to English as ‘Computer Engineering’), introduces a coordinated scheduling optimization model for Rail-Mounted Gantry (RMG) cranes, Automatic Guided Vehicles (AGVs), and yard cranes. This model considers various factors such as interference and task allocation among RMGs, capacity constraints of AGV partners, and energy consumption variations during different AGV states.
One of the key aspects of their research is the introduction of a task assignment strategy that takes into account interference constraints for RMG operations. To solve the model, the researchers proposed an improved hybrid grey wolf genetic algorithm. This algorithm incorporates a grey wolf algorithm position update strategy to enhance the crossover method of the genetic algorithm, improving the efficiency of finding optimal solutions. A reward-based evaluator is also introduced before the selection step of the genetic algorithm to enhance its local search capability.
The results of their experiments showed that considering task allocation for RMGs improved their average utilization rate by 3%-8% compared to scenarios where the RMG operating range is predetermined. Moreover, the improved hybrid grey wolf genetic algorithm reduced energy consumption more effectively within a shorter completion time compared to adaptive chaotic and traditional genetic algorithms.
“This study provides an effective and superior solution for improving efficiency and reducing energy consumption in sea-rail intermodal transportation at container terminals,” said lead author YANG Jiazhu. The research offers valuable insights for maritime professionals seeking to enhance the efficiency and sustainability of their operations.
The commercial impacts of this research are significant. By optimizing the use of RMGs and AGVs, ports can reduce operational costs and energy consumption, leading to more sustainable and profitable operations. The improved utilization rates of RMGs can also lead to faster turnaround times for vessels, enhancing the overall efficiency of sea-rail intermodal transportation.
Furthermore, the improved hybrid grey wolf genetic algorithm developed by the researchers can be applied to other areas of logistics and supply chain management, offering opportunities for further innovation and efficiency gains. As the push for green ports and sustainable logistics continues to grow, this research provides a timely and valuable contribution to the field.
In the words of YANG Jiazhu, “With the growth of multimodal transportation and the push for green ports, improving the efficiency of sea-rail intermodal transportation and reducing energy consumption have become critical concerns for ports.” This research not only addresses these concerns but also paves the way for a more efficient and sustainable future for the maritime industry.