## 1 Introduction
[9] proposed an improved branch price algorithm (BAP) to solve HFGVRPTW, and the results showed that the improved BAP algorithm greatly reduces the branch and calculation time. The model established by [PERSON] and [PERSON] [22] took into account capacity and mileage constraints, time windows constraints, heterogeneous fleets, and time-varying road network conditions and proposed a hybrid algorithm of mixed integer linear programming (MIP) and iterative neighborhood search. 2. The basic idea of the traditional heuristic algorithm is to start from the current solution, search for a better solution in the neighborhood of the current solution and continue to search until there is no better solution. [PERSON] et al. [23] improved the local search stage of the Clarke and Wright heuristic algorithms to solve the two-echelon position path problem (2E-LRP). In the hybrid heuristic algorithm designed by [PERSON] et al. [24], the Clarke and Wright savings heuristic algorithm (CWSHA) and the sweep algorithm were used to generate the initial population continuously. 3. Metaheuristic algorithm is an improvement of the heuristic algorithm, which is the combination of the random algorithm and local search algorithm. [PERSON] et al. [25] proposed an adaptive large-scale neighborhood search algorithm based on simulated annealing. Eight removal operators and four insertion operators were used to search the neighborhood to generate a new solution and simulated annealing acceptance rules were used to determine whether to select the new solution as the current solution. [PERSON] et al. [26] developed a hybrid general variable neighborhood search and tabu search approach to solve the model effectively. Population-based metaheuristics have good global searching ability and can carry out distributed computation conveniently and speed up the solving speed by using inherent parallelism. The latest multi-point heuristic algorithm includes the quantum evolutionary algorithm (QEA) [27], quantum inspired differential evolution algorithm (QDE) [28], improved differential evolution (DE) algorithm [29], etc. However, the local searching ability of the multi-point heuristic algorithm is poor, and the searching efficiency is low in the later stage of calculation. In recent studies, many scholars at home and abroad combine various meta-heuristic algorithms together to form a hybrid meta-heuristic algorithm and make substantial improvements on the basis of some basic algorithms so that the efficiency of the algorithm is greatly improved, and there are abundant achievements in this aspect. This paper designs the algorithm based on the above research results. In particular, this paper focuses on a certain type of GVRP, i.e., the GVRP of fresh agricultural products. There are relatively few previous studies on GVRP of fresh agricultural products. [PERSON] [15] consider the GVRP model of perishable products with operational costs, deterioration costs, carbon emissions, and customer satisfaction as optimization targets, and the objective gradient evolution algorithm was used to solve the model. [PERSON] et al. [30] presented a bi-objective mixed integer linear programming (MILP) model for the vehicle routing and scheduling problem with cross-docking for perishable products. [PERSON] et al. [31] considered the inventory routing problem for perishable products and aimed at maximizing supplier profits and minimizing the cost of fuel consumption, inventory retention, and greenhouse gas emissions, and they developed a precise method based on Bender decomposition to find high-quality solutions in a reasonable time. However, these studies did not focus on the impact of time sensitivity of different fresh agricultural products on distribution planning. At the same time, under different optimization objectives the proportion of freshness loss cost and carbon emission cost to the total cost has not been proven. The model formulated in this paper belongs to the second type of GVRP model, and the algorithm adopted is a hybrid meta-heuristic algorithm.