## 6 Conclusions  
In order to reduce carbon emissions and reduce the total cost of fresh product distribution, this paper constructed the TDGVRPSTW model and designed the variable neighborhood adaptive genetic algorithm to solve the problem. The experimental results which were constructed based on the standard example show that the proposed approach has a faster convergence speed and better optimization ability than the comparison method. Through experiments, this paper found that the geographical distribution of customers will affect the proportion of each part in the total cost. The experiment also explores the influence of the time-sensitive adjustment factor in the freshness loss coefficient function on delivery planning. The last experiment explored the relationship between different costs in the total cost. Based on the experimental findings in this paper, the following recommendations are put forward. 1. In practice, the enterprise should rationally choose the optimization goal and arrange the vehicle distribution plan according to the distribution law of customers, the time-sensitivity of fresh products, the customer time window, and the time-varying road network conditions. 2. Before making a distribution plan, enterprises should have a reasonable estimate of the time sensitivity of fresh agricultural products to be distributed. When agricultural products are time-sensitive, freshness loss has a great impact on the total cost, and enterprises should pay attention to reducing freshness loss. When agricultural products are time-insensitive, freshness loss has little impact on the total cost, and enterprises can give priority to other cost indicators, such as carbon emissions. 3. The proportion of carbon emission cost in the total cost is relatively low, so the carbon trading price can be appropriately raised to attract enterprises to pay attention to the reducing of carbon emission. 4. The preference of enterprises for different optimization objectives in the total cost will significantly affect the total cost, so the weight should be allocated according to the actual situation to achieve the purpose of enterprises. This paper is only an exploratory study on the time-dependent green vehicle routing problem with soft time window constraints. The following cases are worthy of further study. 1. The research in this paper is based on the same fresh agricultural products transported by all vehicles, but in reality, the fresh agricultural products transported by each vehicle may be different, and a variety of fresh agricultural products may be mixed in one vehicle. In these cases, the relationship between freshness loss and total cost, as well as the influence of time sensitivity of agricultural products on distribution plan, need to be further explored. 2. Furthermore, consider the situation of customer orders. A customer's order may contain a variety of fresh agricultural products, and the splitting and merging of customer orders will make transportation more efficient and should be considered, as well as how to do the splitting and merging of orders. These problems require further study. 3. The study in this paper is based on a static scenario, and a study of the dynamic scenario of TDGVRPSTW has not been carried out. The design of research methods in dynamic situations can become the direction of future research. D.W. Conceptualization, Funding acquisition, Supervision, Methodology & Writing--original draft. C.W. : Formal analysis, Validation, Resources, Software & editing. All authors have read and agreed to the published version of the manuscript. This research was funded by the China Education Ministry of Humanities and Social Science Research Youth Fund project (No. 18 YJCZH192), the Applied Undergraduate Pilot Project for Logistics Management of Shanghai Ocean University (No. B1-5002-18-0000), the Open Project Program of Artificial Intelligence Key Laboratory of Sichuan province (No. 2015 RYJ01), and the Social Science Project in Hunan province (No.16 YBA316).