Joint Optimization of Berths and Quay Cranes Considering Carbon Emissions: A Case Study of a Container Terminal in China

The International Maritime Organization (IMO) aims for net zero emissions in shipping by 2050. Ports, key links in the supply chain, are embracing green innovation, focusing on efficient berth and quay crane scheduling to support green port development amid limited resources. Additionally, the energy consumption and carbon emissions from the port shipping industry contribute significantly to environmental challenges and the sustainable development of ports. Therefore, reducing carbon emissions, particularly those generated during vessel berthing, has become a pressing task for the industry. The increasing complexity of berth allocation now requires compliance to vessel service standards while controlling carbon emissions. This study presents an integrated model that incorporates tidal factors into the joint optimization of berth and quay crane operations, addressing both service standards and emissions during port stays and crane activities, and further designs a PSO-GA hybrid algorithm, combining particle swarm optimization (PSO) with crossover and mutation operators from a genetic algorithm (GA), to enhance optimization accuracy and efficiency. Numerical experiments using actual data from a container terminal demonstrate the effectiveness and superiority of the PSO-GA algorithm compared to the traditional GA and PSO. The results show a reduction in total operational costs by 24.1% and carbon emissions by 15.3%, highlighting significant potential savings and environmental benefits for port operators. Furthermore, the findings reveal the critical role of tidal factors in improving berth and quay crane scheduling. The results provide decision-making support for the efficient operation and carbon emission control of green ports.