# Improved GA-LNS Algorithm for Solving Vehicle Path Problems Considering Carbon Emissions
## 5 Analysis and Comparison of Experimental Findings
### Analysis of Results for Different Optimization Objectives  
In order to investigate how various optimization objectives influence the optimal distribution outcomes for the vehicle, while ensuring that other parameters remain unchanged, this study minimizes the vehicle's travelling cost and carbon emission cost as separate objective functions. Simulation experiments were conducted using the R204 and RC104 datasets. The results were then compared to the optimal distribution cost presented in this paper, which has the goal of minimizing the overall distribution expense. The outcomes of the comparison across different optimization objectives are summarized in Table 4.  
The following are shown in Table 4: 1. The GA-LNS algorithm can mostly achieve the optimal solution of a single objective when the vehicle travelling cost and carbon emission cost are minimized. However, this may lead to an increase in other costs, such as the time window penalty cost. This indicates that the algorithm's iterative solution process prioritizes other single objective functions at the expense of customer satisfaction, resulting in the vehicle deviating from the original distribution expectations of the customer or the satisfactory service time window. This, in turn, leads to decreased customer satisfaction, resulting in customer loss, which is detrimental to the enterprise's long-term development. 2. In example R204, a comparison of the total costs obtained by minimizing the driving cost and minimizing the carbon emission cost reveals that The total costs obtained with the objective of minimizing total costs are reduced by21.15% and25.25%, respectively; in example RC104, minimizing the total costs results in reductions of 18.58% and 25.05% compared to minimizing the driving cost and minimizing the carbon emission cost, respectively. These findings demonstrate that the model established in this paper effectively accounts for vehicle traveling costs and carbon emission costs, balances the trade-offs among various objectives, and attains the objective of reducing the overall distribution expenses by reconciling multiple influencing factors.  
This research outcome has a positive impact on the route optimization of logistics vehicles, as well as on energy-saving and carbon reduction strategies in China. On one hand, by applying the GA-LNS algorithm, logistics enterprises can effectively reduce carbon emissions and promote the advancement of green logistics while simultaneously controlling distribution costs. This approach not only assists companies in fulfilling their social responsibilities and meeting the requirements of national environmental protection policies but also enhances their market competitiveness. On the other hand, the optimized vehicle routes better address customer service needs, thereby improving customer satisfaction and reducing customer churn, thus creating favorable conditions for the sustainable development of the company. Ultimately, this study provides robust support for the formulation and implementation of policies within China's logistics industry, promoting the green transformation of the sector and facilitating the high-quality development of the economy.