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Abstract To effectively solve the reverse logistics distribution problem caused by the increasing number of scrapped parts in the automotive market, this study constructs a multi-trip green vehicle routing problem model with time windows by comprehensively considering the coordination between carbon dioxide emissions and cost efficiency. A hybrid adaptive genetic algorithm is proposed to solve this problem, featuring innovative improvements in the nearest neighbor rule based on minimum cost, adaptive strategies, bin packing algorithm based on the transfer-of-state equation, and large-scale neighborhood search. Additionally, to efficiently obtain location data for supplier factory sites in the distribution network, a coordinate extraction method based on image recognition technology is proposed. Finally, the scientific validity of this study is verified based on the actual case data, and the robust optimization ability of the algorithm is verified by numerical calculations of different examples. This research not only enriches the study of green vehicle routing problems but also provides valuable insights for the industry to achieve cost reduction, efficiency enhancement, and sustainable development in reverse logistics.

COVID-19 is a major public health emergency, the Chinese government adopts pandemic prevention and control measures such as home quarantine in order to block the strong contagiousness of the virus. In order to solve the problem of distribution difficulties due to the limited transportation resources brought by the pandemic and to guarantee the demand of fresh agricultural products for the isolated residents, this study takes the emergency supply as the main objective, and designs three distribution modes, namely, single distribution center, multi-vehicle model based on the classification of temperature zones, and multi-distribution center for isolated communities of different sizes, with the objectives of minimizing the number of vehicles in use, minimizing the average response time, and minimizing the risk of viral infections. A multi-objective optimization model was constructed as the objectives. Then the genetic algorithm is improved by adding destruction operator, repair operator and greedy operation, and the effectiveness of the algorithm is verified through empirical research, and the advantages of the algorithm in convergence speed are compared and analyzed. Finally, different distribution methods are suggested for the number of communities of different sizes. This study can provide a scientific basis for government departments to formulate an optimized decision-making plan for the emergency supply and distribution of fresh agricultural products under public health emergencies.

In the context of the Chinese government’s policy guidance, there is black soil protection and ecological environment protection. The purpose of this paper is to solve the problem that the soil ecology of the black soil in Northeast China is changing year by year, and it is necessary to explore the sowing and fertilization strategy under the new situation; most Chinese growers rely excessively on their personal experience in the process of soybean sowing and fertilization. In this study, we used “Heihe 43” soybeans and used regression experimental design methods to analyze the effects of planting density, nitrogen, phosphorus, and potassium fertilizer application on soybean yield and to determine the optimal planting density and fertilizer ratios. The study reveals that the optimal soybean planting density in Northeast China is 45.37 × 104 plants/ha, with nitrogen at 98.4 kg/ha, phosphorus at 218.96 kg/ha, and potash at 47.62 kg/ha. Under these conditions, soybean yields can reach 3816.67 kg/ha. This study can provide a theoretical method for decision-making to obtain the optimal planting density and fertilizer ratio for different regions of the farming system.

With the continuous development of agricultural product e-commerce platforms and the rapid growth of trading volume, conducting in-depth mining and analysis of online review data to improve consumer satisfaction is of considerable importance. This paper uses JingDong’s self-run online reviews of rice agricultural products as the research object and analyzes the logistics factors that affect consumer satisfaction through text mining technology. A multi-objective model for logistics center distribution path optimization under a soft time window was constructed. The model used the results of online review analysis, namely, packaging integrity, delivery timeliness, and logistics cost, as the goals, and the model used ant colony algorithm (ACO) and genetic algorithm (GA) to solve the optimal distribution solution to minimize the penalty cost and transportation cost. Through examples to solve the optimal distribution vehicle number and shipping routes, in addition, a comparison of the two types of algorithm performance of the model under different node number indicated that the number of nodes affects algorithm performance. With a node number below 50, the ant ACO has high precision and a better distribution path. With a node number above 50, GA has more comprehensive performance. The average efficiency of the GA is 12.28% higher than that of ACO.