# A Study on the Vehicle Routing Planning Method for Fresh Food Distribution
###### Abstract
[14] designed an adaptive biogeographical optimization algorithm for optimizing storage settings, involving multiple categories and temperatures in the logistics and distribution. [PERSON] et al. [15] modeled a cold chain system for fruit and vegetable rich regions, analyzed the obstacles and problems in traditional fruit and vegetable distribution channels, and created new distribution networks. In response to the problem of location in the product supply chain, [PERSON] et al. [16] adopted a pseudo reverse spider monkey optimization algorithm based on the Laplace distribution (LOBSMO) to solve multi-dimensional complex logistics distribution center location problems. [PERSON] et al. [17] used triangular fuzzy numbers to represent the uncertainty of demand, weighted time, and cost and established a multi-level emergency logistics network. [PERSON] et al. [18] proposed a mixed integer programming (MIP) algorithm for addressing the location of facilities in emergency logistics and meeting various material distribution needs in disasters. The above research separates distribution vehicle routing optimization problems from distribution center location problems and can only obtain the results for a single type of problem, which cannot meet the requirements of overall optimization. In the process of distribution in cold chain logistics networks, considering collaborative optimization between these two aspects can maximize the benefits of the overall distribution system. In recent years, domestic and foreign scholars have continuously deepened their research on agricultural product supply chains, comprehensively considering location and routing problems (LRPs), which are divided into single-layer location routing problems, double-layer location routing problems, and multi-layer location routing problems according to the node types. [PERSON] et al. [19] studied a location routing problem with its time windows constrained by the volume of distribution centers, using a two-stage algorithm to determine the location of the distribution centers by introducing space-time distance through a k-means clustering algorithm and then using particle swarm optimization to obtain the optimal distribution path for each distribution center. [PERSON] et al. [20] considered the complex dynamic requirements when conducting location route optimization and proposed a two-stage model. In the first stage, the initial distribution path was designed based on alternative front warehouse locations. After running for a while, the second stage entered the dynamic optimization stage, where real-time new demand points were optimized and calculated to adjust the distribution path. [PERSON] et al. [21] designed a hybrid heuristic algorithm based on a savings algorithm and adaptive large neighborhood search for differences in the status of returned goods while allowing replenishment operationswithin the front warehouse and taking into account the location of the front warehouse. They proposed improved operators to effectively improve the algorithm speed, reduce the number of vehicles used and the logistics delivery costs. To sum up, current domestic and foreign scholars often focus on the optimization of transportation routes for fresh product logistics network planning and ignore the impact of selecting appropriate locations to establish front warehouses on the entire logistics network distribution. Both location and route optimization are closer to the actual distribution mode, and considering only one aspect makes it difficult to achieve an optimal solution for overall logistics network planning. In studies that consider both the location of front warehouses and the transportation path, the problem of location is optimized, and the path optimization is performed after obtaining the determined location, without considering the interaction between them.