# Ship Type Selection and Cost Optimization of Marine Container Ships Based on Genetic Algorithm
## 1 Introduction
[9] further studied the optimization problem of short-term liner vessel speed and route allocation, linking optimal speed with optimal sailing time for different routes and constructing a mixed-integer nonlinear programming model. [PERSON] [14] analyzed the emission reduction effects of lowering vessel speed under different vessel types and route conditions, noting that when the price of heavy fuel oil exceeds the breakeven point (i.e., USD 260-470 per ton), container liner fleets tend to reduce speed, resulting in a 11% reduction in CO2 emissions within two years. [PERSON] et al. [15] introduced sailing speed as a decision variable to construct a new route allocation model. [PERSON] et al. [16] studied the joint tactical planning problem of vessel numbers, maximum planned speed, and liner service plans, establishing relationships between objectives and decision variables and proposing a non-dominated sorting genetic algorithm. Regarding studies on route allocation under carbon reduction policies, with more and more attention being paid to environmental protection, carbon reduction has become an inevitable part of various transportation behaviors. [PERSON] et al. [17] analyzed three emission reduction measures--installing scrubbers, using distillate oil, and switching to low-sulfur fuel--based on annual fuel consumption for different vessel types and operating modes. [PERSON] [18] proposed a bi-level programming model for deep-sea container vessel operations, converting carbon emissions into carbon taxes with the aim of minimizing vessel operating costs. [PERSON] et al. [19] studied speed optimization issues considering vessel carbon emissions, demurrage fees, and vessel scheduling, establishing speed optimization models under three different forms of carbon tax and verifying the model's effectiveness through examples. [PERSON] et al. [20] examined optimal order quantities and transport mode selection under different carbon reduction policies, considering customer demand uncertainty. [PERSON] et al. [21] analyzed the main factors influencing shipping companies' implementation of green shipping through structural equation modeling and explored the effects of green shipping on environmental and economic performance. [PERSON] et al. [22] proposed a stochastic programming model for route allocation, constructing 12 scenarios with different carbon prices to analyze the impact of maritime carbon emission trading policies on container fleet deployment and carbon emissions. [PERSON] et al. [23] studied the route allocation and speed optimization problem under carbon tax and carbon cap policies, establishing a nonlinear programming model with the objective of minimizing weekly operational costs. Despite extensive research on route allocation, including aspects such as speed optimization, vessel allocation, and carbon reduction strategies, several key areas still lack comprehensive investigation. Firstly, most current research often separates ship type selection and cargo allocation, ignoring their interdependencies. The complex relationship between vessel type selection and cargo allocation is crucial for optimizing route allocation but lacks systematic study. Secondly, while the impact of carbon emission policies on shipping operations has been acknowledged, research on bi-level programming models that simultaneously aim to minimize company operational costs and carbon emissions under carbon emission policy contexts is still limited. Particularly, integrating upper-level operational cost minimization with lower-level carbon emission optimization using energy efficiency indices is rare. Therefore, the challenge of effectively modeling and solving joint optimization of vessel type selection and cargo allocation while balancing cost and carbon emission goals remains an area for further exploration. In response to these gaps, this paper proposes a bi-level programming model for vessel selection in different container liner routes to analyze route allocation optimization under carbon reduction policies.