## 1 Introduction  
In recent years, climate change and carbon emissions have gradually become a common challenge for human society. In the work report of the Chinese government in 2021, carbon peak and carbon neutralization were first mentioned and immediately became the focus of social discussion. According to the _CO\({}_{2}\) Emissions from Fuel Combustion: Overview_ report released by the International Energy Agency in 2020, global carbon dioxide emissions reached a record 33.5 billion tons in 2018, with transport accounting for 24 percent of the total at 8.04 billion tons, while road transport accounted for three-quarters of the total carbon emissions in the transport sector. Therefore, it is of great practical significance to deeply study the green vehicle routing problem and promote energy- saving and emission reduction in the delivery of fresh products. Previous studies on the green vehicle routing problem (GVRP) can be divided into three categories: vehicle routing problem related to reverse logistics [1], vehicle routing problem related to new energy vehicles, and fuel vehicle routing problem aiming to reduce environmental pollution. The GVRP problem defined in this article refers only to the last one. [PERSON] and [PERSON] [2] proposed the pollution routing problem (PRP) for the first time in 2011. Green vehicle routing problem (GVRP) was first proposed by [PERSON] et al. [3] in 2012. Although the two concepts have different names, they are essentially the same, and this paper defines them both as GVRP. However, relevant studies can be traced back to the relationship between vehicle fuel consumption rate and driving speed proposed by [PERSON] and [PERSON] in 2005 [4]. Subsequently, many scholars have conducted in-depth research on this issue. The literature can be divided into three categories based on different objective functions, and the three categories are as follows. 1. The first category takes carbon emission or fuel consumption as the optimization target. Aiming to minimize fuel consumption, [PERSON] (2016) established the pollution routing problem (PRP) model with capacity and vehicle number constraints. [PERSON] et al. (2016) established a multi-depot vehicle routing problem (MDVRP) model that can share depot resources. Considering that the speed of vehicles on various sections depends on the time of departure and the time period in which the vehicles are travelling, [PERSON] and [PERSON] (2016) established the time-dependent vehicle routing problems (TDVRP) model and allowed multiple paths to be selected between nodes; aiming to minimize carbon emissions, [PERSON] et al. (2016) used the emission factor model to convert the mileage of vehicles into carbon emissions. [PERSON] et al. (2016) constructed the heterogeneous fleet green vehicle routing problem with time windows (HFGVRPTW). [PERSON] et al. (2016) considered that vehicle speed changed with different time periods and road sections. The vehicle speed was defined as a random variable, and the influence of speed and load on the path to carbon emission minimization was analyzed. A TDVRP model with vehicle numbers constraint was constructed. 2. The second type takes environmental cost and economic cost as the optimization target. [PERSON] et al. (2016) built models including maximum vehicle capacity, speed, carbon emissions, asymmetric paths, and time windows constraints, and applied the technique for order performance by similarity to ideal solution (TOPSIS) technology to integrate economic and environmental factors. TOPSIS is a criterion for selecting the most suitable solution. [PERSON] et al.