# Operation Optimization of the Sea Container Fleet Based on the Double-Level Planning Model
## 1 Introduction  
Ocean container transportation has gained increasing significance in contemporary sea transportation, mainly due to its convenient and efficient nature [1]. In addition, with the continuous progress of green ship technology and the continuous instability of the global shipping market [2], shipping companies must adopt an optimal route configuration, voyage, and speed design to improve the capacity of container ships and achieve the maximum output or efficiency with the least input or consumption [3].  
In order to achieve the reasonable allocation of container ship operation planning on multiple routes, [PERSON] et al. [4] used the self-correlation feature scheduling method based on correlation rule information fusion to improve the automatic dispatching ability of container route dynamic loading. [PERSON] [5] established an optimization model to allocate time windows and appointment quotas for each ship at sea freight container terminals. [PERSON] et al. [6] proposed a fixed route speed optimization model that distinguished water speed from land speed to minimize the total fuel consumption of the voyage. [PERSON] et al. [7] used the fuzzy quality function expansion (QFD) method to construct an evaluation index system for the collaborative operation level of container shipping alliances and improved shipping efficiency by improving the management operation level. [PERSON] et al. [8] used the saving matrix method to determine the distribution route to minimize transportation costs. [PERSON] et al. [9] used a machine learning paradigm to solve the prediction difficulties of ship detention. [PERSON] et al. [10] established a joint optimization model for shipping network design-infrastructure investment and minimized the generalized transportation costs in the study area by jointly optimizing the container port's throughput and shipping network operation plans. [PERSON] et al. [11] proposed an ellipsoid uncertainty set to study the impact of low sulfur fuel costs on container liner scheduling. [PERSON] et al. [12] proposed an improvedthree-dimensional dynamic programming algorithm for ship route optimization with weather and other constraints to obtain the optimal ship route.  
The scattered nature of research on various aspects of ports, ships, and fuel, as mentioned above and as observed by [PERSON] et al. [13], is a primary cause of the inefficiency of shipping optimization models. [PERSON] et al. [14] and [PERSON] et al. [15] separately examined and studied the classification of maritime logistics and the integration of shipping logistics, and they concurred that integrating each logistics link in shipping would be a key research direction in the future. Simultaneously, ocean transportation-induced environmental pollution has sparked concern, leading to heightened international expectations regarding carbon emission issues [16]. Currently, there is a lack of sufficient quantitative analysis on factors like carbon tax [17].  
Based on this, considering the carbon tax cost, meeting freight demand, and ensuring transportation income, the scientific novelty of this paper is to build a comprehensive optimization model for container ship configuration, route, and speed based on nonlinear programming aimed at reducing the overall operation costs of ships. In view of the complexity of the model, this paper formulates the problem as a multi-objective optimization problem [18], uses the discrete algorithm, and analyzes the effectiveness and sensitivity of the model based on basic data of ship speed on specific routes to prove that this model can provide practical support for optimizing container fleet operation strategies.