Xiangyu Li’s research while affiliated with Jilin University and other places

New concepts such as low-carbon economy, low-carbon production, low-carbon living and even low-carbon cities have become popular topics in environmental protection. The disassembly line part of reverse logistics is accompanied by high carbon emission, which is contrary to the original intention of sustainable development. In this paper, we design a systematic low-carbon layout for the disassembly line of the logistics processing center to address the problem of high carbon emissions caused by the unreasonable layout of the disassembly line. Taking the disassembly line in the logistics center of Company H as the research object, the process of the disassembly line is analyzed, and the SLP analysis method is applied to analyze the material flow and the material flow intensity level of the disassembly line layout, and three different optimization schemes are derived. Flexsim software was used to model and run the three initial layout schemes of the disassembly line, and the data related to the waiting time operation of each scheme were obtained. Finally, carbon emission and other disassembly-line-related indicators were introduced and weights were set, and the results were subjected to weighted gray correlation analysis to arrive at the optimal disassembly line layout optimization scheme. This study will provide reference for other reverse logistics processing center layout studies.

In this paper, we take the four-way shuttle system as the research object and establish the mathematical model of scheduling optimization based on the minimum time for the in/out operation optimization and path optimization scheduling problems of the four-way shuttle system. An improved genetic algorithm is used to solve the task planning, and an improved A* algorithm is used to solve the path optimization within the shelf level. The conflicts generated by the parallel operation of the four-way shuttle system are classified, and the improved A* algorithm based on the time window method is constructed for path optimization through the dynamic graph theory method to seek safe conflict-free paths. Through simulation example analysis, it is verified that the improved A* algorithm proposed in this paper has obvious optimization effect on the model of this paper.

In the context of sustainable development, this paper rationalises the outbound process of a four-way shuttle system with a focus on their modelling, performance evaluation and configuration using a parallel operation strategy to reduce resource waste, thus achieving sustainable development. The parallelism of the hoist and shuttle is innovatively incorporated into the four-way shuttle system, so the modelling content is divided into parallel and serial types. In the parallel operation strategy model, a separation–aggregation queueing network model is constructed, and the open-loop queueing network is innovatively solved using the maximum entropy method. In the serial operation strategy model, a semi-open-loop queuing network is constructed and solved using the geometric matrix method. By varying different parameters, the accuracy of the model is verified by Arena simulation with an error range of 10% or less, and the error of the system performance index calculation is reduced by 20% compared with the existing methods. Setting up 18 different sizes of shuttle systems provided a better performance than a single serial-operation strategy through the addition of parallel strategies, with an average reduction of 12.6% in the system response time and a minimum reduction of 1.8%. The conclusions of this paper were verified on the basis of an arithmetic case analysis.

Straw burning can cause serious environmental pollution, whereas returning straw to the fields, as a green production method, can improve the rural environment and strengthen the sustainable development of agriculture. According to statistics, China produced 797 million tons of straw in 2020, but the current straw return technology still needs to be improved; the ability of farmers to choose the correct amount of straw to be returned to the field and their awareness of environmental protection still need to be strengthened. Straw is still openly burned in some areas, causing environmental pollution and the waste of resources, which are contrary to the concept of sustainable development in China. In this study, we estimated the amount of straw resources in Heilongjiang Province, a major grain-producing province in China, by quantifying the production of major crops between 2011 and 2020. We then identified and analyzed the current problems in terms of policy support and other aspects. We used an integrated AHP-fuzzy evaluation method to evaluate the comprehensive benefits of different straw return amounts, and we determined the amount of straw that should be returned to the soil to produce the best comprehensive benefits. We provide suggestions for the current main problems regarding the amount of crop straw to return to the soil in Heilongjiang Province, arguing that choosing a reasonable straw return amount will help farmers increase profit, reduce environmental pollution, and contribute to the sustainable development of the environment.

With the development of China’s express delivery industry, the number of express packaging has proliferated, leading to many problems such as environmental pollution and resource waste. In this paper, the process of reverse logistics network design for express packaging recycling is given as an example in the M region, and a four-level network containing primary recycling nodes, recycling centers, processing centers, and terminals is established. A candidate node selection model based on the K-means algorithm is constructed to cluster by distance from 535 courier outlets to select 15 candidate nodes of recycling centers and processing centers. A node selection model based on the NSGA-II algorithm is constructed to identify recycling centers and processing centers from 15 candidate nodes with minimizing total cost and carbon emission as the objective function, and a set of Pareto solution sets containing 43 solutions is obtained. According to the distribution of the solution set, the 43 solutions are classified into I, II, and III categories. The results indicate that the solutions corresponding to Class I and Class II solutions can be selected when the recycling system gives priority to cost, Class II and Class III solutions can be selected when the recycling system gives priority to environmental benefits, and Class III solutions can be selected when the society-wide recycling system has developed to a certain extent. In addition, this paper also randomly selects a sample solution from each of the three types of solution sets, conducts coding interpretation for site selection, vehicle selection, and treatment technology selection, and gives an example design scheme.

With the development of e-commerce and trade, China’s logistics transportation demand has increased significantly. To improve the operation efficiency of new energy trucks, logistics transportation companies need scientific management methods. They need to analyze a large number of real driving conditions for new energy trucks. Additionally, to reduce new energy trucks’ energy consumption and pollutant emissions, automobile manufacturers have increased the research and development of new energy trucks, and the analysis of new energy truck driving conditions is the basis for the technical development and evaluation of new models. The research in this article is based on an actual project of an automobile manufacturing company, consulting a large amount of relevant domestic and foreign literature, summarizing the current status of driving conditions at home and abroad, explaining the principle of data collection for the driving conditions of new energy trucks, and developing in-vehicle data for driving conditions based on the principle transmission method and remote transmission method. Using the membership function and K-means clustering algorithm to determine the attribute characteristics of the new energy truck driving condition analysis, a truck driving condition analysis model is built, the software function of the model is designed, and a small amount of sample data is used to import the model instance to verify the model effectiveness. Finally, based on the constructed new energy truck driving condition analysis model, big data technology was used to perform a big data analysis experiment on the actual operation data of 200 trucks of an automobile manufacturing enterprise. The Spark big data calculation framework was used to perform stream calculation and offline analysis calculations on a large amount of data from the new energy trucks. The results show that the operating efficiency of the new energy truck driving condition analysis method using big data technology is significantly higher than that of traditional technology. This study provides a theoretical basis for controlling the energy consumption and pollutant emissions of new energy trucks in logistics transportation, and provides management and logistics support for transportation logistics companies. The technical development and evaluation of the company’s new models provided data references.

Mao, J.; Hong, D.; Ren, R., and Li, X., 2020. The effect of marine power generation technology on the evolution of energy demand for new energy vehicles. In: Yang, Y.; Mi, C.; Zhao, L., and Lam, S. (eds.), Global Topics and New Trends in Coastal Research: Port, Coastal and Ocean Engineering. Journal of Coastal Research, Special Issue No. 103, pp. 1006–1009. Coconut Creek (Florida), ISSN 0749-0208.With the rapid development of social economy, the environmental problems are becoming more and more serious. Under such context, green economy development, especially the development of new energy vehicles has attracted increasing attention by all countries. Marine energy generation technology plays an important role in promoting the development of new energy vehicles as it provides a strong source of energy. A new energy vehicle energy demand forecasting model is constructed and a new energy vehicle energy demand evolution analysis algorithm under ocean energy generation technology is proposed. The statistical sequence distribution model for energy demand of new energy vehicle energy is proposed. The association rules of energy demand of new energy vehicle are extracted. Fuzzy clustering method to automatically cluster the big data of new energy vehicle energy demand under ocean energy generation technology is adopted, the optimized iterative model of new energy vehicle energy demand forecast is established, the adaptive optimization algorithm is combined to realize the new energy vehicle energy demand forecast. The simulation results show that the proposed method is more effective in forecasting the energy demand for new energy vehicle development.

In this paper, based on the clay brick of building waste, nano-titanium dioxide photocatalytic decorative concrete was prepared, and its application in the pavement structure of a railway freight yard was studied. The results show that the loading capacity of nano-titanium dioxide on clay sand carrier is better than that on zeolite sand. With the increase of powder content, the photocatalytic efficiency of decorative concrete surface decreases, but the degree of reduction is small. Compared with laboratory conditions, the reduction rate of NO X in surface layer of decorative concrete under natural curing condition is lower. The photocatalytic efficiency did not decrease significantly at 90 days. Cement cementing could greatly improve the bonding effect of nano-titanium dioxide, which made the photocatalytic activity of decorative concrete surface layer have good durability. In this paper, decorative concrete is applied to the pavement engineering of a railway container yard in Liaoning Province. The pavement structure combination design, cement concrete pavement thickness design, pavement panel design and joint design are analyzed.