###### Abstract
studied the spatial scheduling problem of a ship painting operation, which aimed to consider the allocation of painting workshop and space and balance the workload of the painting team, propose an algorithm of spatial scheduling, and develop a corresponding system. The experiment proved that the system solves the space scheduling problem of ship segmental painting well and balances the workload of the painting team [6]. It can be seen that the existing scheduling methods in ship construction face many challenges, such as the underutilization of time, space, and resources. It is worth noting that shipping companies are facing a green transition due to the huge increase in carbon emissions and energy consumption costs [7; 8; 9]. Therefore, adopting low-carbon concepts and intelligent algorithms to realize low-carbon scheduling of ship segment painting workshops is of great significance for enhancing China's ship manufacturing industry and is also an important way to promote the development of intelligent and green ship painting. Researchers have conducted a large number of studies to address the low-carbon scheduling problem. For example, [PERSON] et al. developed a scheduling model based on carbon capture, carbon emissions trading demand response, and renewable energy generation to solve the low-carbon economic scheduling problem of the power-gas system, studied the economic and carbon emission benefits under the synergy of different low-carbon technologies, balanced the economy, carbon emission, and risk by setting the confidence level reasonably, and experimentally proved the feasibility and effectiveness of the proposed model by applying the case study to the proposed model [10]. [PERSON] et al. aimed to reduce unnecessary energy consumption in the industrial sector by investigating the shutdown mechanism of whether and when to turn off the power supply and set different speed levels and proposed a green scheduling algorithm to solve the problem, and the case results validated the performance of the algorithm [11]. [PERSON] et al. considered the furniture manufacturing industry's carbon scheduling problem and developed a hybrid optimization algorithm with sequential hybridization and experimentally analyzed the strategies introduced by this algorithm, and the results show that it can reduce the carbon footprint by 9.82% [12]. In summary, scholars have considered the energy consumption and carbon emissions of various industries, such as electric power and furniture manufacturing, at the levels of model constraints, machine optimization, and scheduling strategies. However,there are few reports in the field of ship construction involving low-carbon scheduling for painting. The high demand for low-carbon scheduling is highlighted by the complexity of the ship segmental painting process and the varying levels of productivity and energy consumption generated between painting operators and equipment. The low-carbon scheduling of ship-segmented painting considers segmental sequencing along with the selection of the painting team, and the complexity of the problem is more complicated than the classical hybrid flow-shop scheduling problem (HFSP). Since the past research on HFSP mainly focuses on a single objective function and does not take into account the real problems, research on multi-objective problems (MOPs) has been increasing year by year [13; 14]. [PERSON] et al. systematically examined and evaluated a series of clustering algorithms and models in order to solve the problem of accurate customer segmentation for the financial card industry, and the experimental results showed that the method could deepen the segmentation of customers and promote economic growth [15]. [PERSON] et al. provided a proper medical supervision model for optimizing tumor treatment. The model was developed to minimize the cancer cell density and the number of approved drugs and solved using a meta-search algorithm, and the experimental results show that the algorithm has superior performance in terms of convergence and scalability [16].