## 1 Introduction
(2016) took the speed as a continuous decision variable, adopted the road section speed optimization strategy to make vehicles run at the optimal speed in each road section, and took the minimization of the total cost composed of fuel consumption cost and driver's salary as the optimization objective, respectively, and constructed a PRP model and open green vehicle routing problem with time windows (GVRPTW) model with vehicle numbers and time window constraints. Aiming at the one-to-one pickup and delivery problem, [PERSON] et al. (2016) constructed a heterogeneous VRPTW model with the optimization objective of minimizing the total cost composed of fuel consumption cost, driver wage cost, and penalty cost for violating the time windows, considering that vehicle speed varies with urban and non-urban sections. 3. The third category takes two or more conflicting optimization objectives as objective functions. [PERSON] and [PERSON] (2016) assumed that customer demand was a fuzzy number simulated by a time-dependent algorithm and established a multi-objective fuzzy chance-constrained programming model. [PERSON] and [PERSON] (2016) established a multi-objective heterogeneous VRPTW model with fuel consumption, minimizing vehicle use and maximizing customer satisfaction as optimization objectives. [PERSON] et al. (2016) constructed a multi-objective GVRPTW model of perishable products, with operating cost, deterioration cost, carbon emission minimization, and customer satisfaction maximization as optimization objectives. [PERSON] et al. (2016) constructed a multi-objective PRPTW model for heterogeneous VRPTW with the optimization objectives of minimizing total fuel consumption and total driving time and maximizing the number of customers served. In the literature on the vehicle routing problem with time windows, some literature explored the relationship between time windows and pollution emission (2016); (2016); (2016). Representative works include the following: [PERSON] et al. (2016) analyzed the impact of two different distribution policies on carbon emissions and proved that the VRPTW model had lower carbon emissions than the DCVRP model at the same time interval through practical cases. However, they used the emission factor model to estimate carbon emissions, and simply converted the distance of vehicles into carbon emissions without considering the impact of vehicle speed, load, slope, and other factors on carbon emissions. [PERSON] et al. (2016) proposed an approach using a mixed-integer linear programming model to assess the impact of extended delivery times, using data from real cases to analyze the impact of different extended delivery times on distance traveled, transportation costs, and carbon emissions. Experimental results show that delivery times of up to four days can save 57% of total mileage, 61% of total cost, and 56% of fuel consumption and/or CO\({}_{2}\) emissions. However, the experiment lacked the overall consideration of the three optimization objectives, and its case scale was small, so the method is not suitable for solving large-scale problems. [PERSON] et al. [21] established a TDVRPTW model with the optimization objective of minimizing the sum of driver cost, fuel consumption, and carbon emission, and proposed an improved ant colony algorithm (IACACAA) with a congestion avoidance method to solve the model. Experiments were designed to compare the large time window and the tight time window, proving that cases with short service time and a large time window could obtain vehicle routes with lower total cost and thus reduce carbon emissions. For the above three types of GVRP models, solving algorithms can be divided into the precise algorithm, traditional heuristic algorithm, and meta-heuristic algorithm. The specific summary is as follows. 1. The precise algorithm is one that can find an optimal solution to a problem. [PERSON] et al.