# A Study on the Vehicle Routing Planning Method for Fresh Food Distribution
###### Abstract  
Aimed at the high cost of cold chain distribution of fresh agricultural products within a specified time window, a joint optimization method based on a bi-level programming model for cold chain logistics is proposed for the location of front warehouses and distribution path planning. At the upper level of the bi-level programming model, k-means clustering analysis is used to obtain all accurate information about alternative locations for the front warehouse for site selection, thereby providing the corresponding foundation for the lower level algorithm. At the lower level of the model, a fusion algorithm of particle swarm optimization (PSO) and a genetic algorithm (GA) is used for solving. To accelerate the convergence speed of the population and lower the running time of the algorithm, the parameter values in the algorithm are determined adaptively. An adaptive hybrid algorithm combining the particle swarm optimization algorithm and the genetic algorithm (APSOGA) is used to reallocate the location information on backup points for the front-end warehouse, ultimately determining the facility location of the front-end warehouse and planning the end path from the front-end warehouse to the customer point, achieving joint optimization of the front-end warehouse's location and path. A comparative analysis of algorithm optimization shows that using the APSOGA hybrid algorithm can reduce the total cost of the logistics network by 14.57% compared to a traditional single-algorithm PSO solution and reduce it by 5.21% compared to using a single GA. This proves the effectiveness of the APSOGA hybrid algorithm in solving location and path planning problems for cold chain logistics distribution companies. f +
Footnote †: journal: Applied sciences  
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Footnote 1: College of Transportation Engineering, Dalian Maritime University, Dalian 116026, China  
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Footnote 2: School of Mechanical Engineering and Automation, Dalian Polytechnic University, Dalian 116034, China  
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Footnote 2: School of Mechanical Engineering and Automation, Dalian Polytechnic University, Dalian 116034, China
warehouses can optimize the entire supply chain network, thereby minimizing the costs and maximizing the benefits. In order to provide convenient route planning services for the multi-point distribution of fresh agricultural products, a distribution route planning system for delivering fresh agricultural products in the shortest time is proposed [4]. [PERSON] et al. [5] focused on summarizing an algorithm based on reinforcement learning and deep reinforcement learning methods for solving the path planning problem for autonomous vehicles; classified the algorithm according to value-based and policy-based methods; and analyzed the characteristics, benefits and drawbacks, and improvement measures for each type of algorithm. [PERSON] and [PERSON] et al. [6; 7] proposed planning methods for the distribution paths for emergency supplies. For vehicle path optimization, [PERSON] et al. [8] designed an ant colony algorithm that incorporates a knowledge model of tabu search operators and dynamic probability selection under a knowledge-based elite strategy to solve cold chain path optimization problems. [PERSON] et al. [9] developed a method for accurately calculating the probability of route success, which uses a branch-and-cut algorithm to solve vehicle routing problems involving hard time windows and random service times. [PERSON] et al. [10] used the ideal point method, a penalty function, and a Pareto optimal solution set to optimize vehicle delivery routes involving multiple objectives and multiple time windows, reducing the vehicle transportation costs, shortening the transportation distances, and reducing the number of transportation carriers. [PERSON] et al. [11], [PERSON] et al. [12], and [PERSON] et al. [13] combined tabu search and a differential algorithm based on 2-OPT \(\epsilon\)--an approximate algorithm for performing constrained scheduling. [PERSON] et al.