Cheng Zhang

With the increase in carbon emissions from railway transit, green transportation has attracted worldwide attention due to its low pollution and low consumption. In order to improve the transportation efficiency of multimodal transport and reduce carbon emissions, this paper makes a systematic study on the comprehensive optimization model and method of multiple transport tasks and transport modes considering carbon emissions. Firstly, an optimization model is established with transportation distance, transportation time, and carbon emission as transportation objectives. Secondly, an improved fuzzy adaptive genetic algorithm is designed to adaptively select crossover and mutation probabilities to optimize the path and transportation mode by using population variance. Finally, an example is designed, and the method proposed in this paper is compared with the ordinary genetic algorithm and adaptive genetic algorithm, which proves the proposed model and algorithm are effective. In conclusion, it is found that the present multi-objective optimization model based on the improved genetic algorithm can adjust multimodal transport plans and reduce carbon dioxide emissions, which provides a reference basis for logistics enterprises to carry out multimodal transport.

In order to study the mixed time window vehicle routing optimization problem based on customer priority, a customer differentiation management strategy based on customer priority is proposed. Combined with the main factors affecting customer priority evaluation and the characteristics of vehicle routing problem with mixed time windows, a comprehensive evaluation index affecting customer priority was first established and DBSCAN clustering algorithm was used for clustering analysis of customer priority to solve the optimization problem of cold chain distribution route considering customer priority. Fuzzy time window of refrigerated vehicles was then constructed with trapezoidal fuzzy number, and a mathematical programming model was built with an objective function for minimizing the sum of fixed, green, penalty, refrigeration, and cargo damage costs. Two scenarios of out-of-stock and not-out-of-stock were designed. Finally, an improved genetic algorithm was used to solve the model, and the rationality of the model was verified through a case of imported fruit distribution in Xiamen City. Results showed that the proposed method can effectively solve the routing problem of refrigerated trucks considering customer priority. Moreover, the findings of this study can provide a new approach for solving the routing optimization problem of refrigerated trucks considering customer priority.

The prevention of railway operational accidents has become one of the leading issues in railway safety. Identifying the impact factors which significantly affect railway operating is critical for decreasing the occurrence of railway accidents. In thi...

Waste household appliances and electronic products usually contain harmful substances which need scientific and reasonable collection, classification, processing, recovery and disposal to achieve sustainable and effective recycling and utilization. I...

Given the time-efficient characteristics of urban cold chain transportation and the time-varying characteristics of urban road speed, customers encounter the problem of limited vehicle path optimization due to a fuzzy time window. An optimization model of urban cold chain transportation with the objective function as the minimum total cost is constructed under the premise of service reliability, and an artificial immune particle swarm optimization algorithm is designed to solve the model. For an empirical analysis of Xiamen’s cold chain transportation, a two-stage solution involving “static optimization and dynamic optimization” is used to verify the effectiveness of the model and the practical value of this research. Results show that the time-varying model can effectively reflect the situation of urban road transportation and satisfy the timeliness requirement of urban cold chain transportation.

With the continuous advancement of China’s supply-side structural reform, the country’s energy consumption structure has undergone considerable changes, including an overall reduction in fossil energy use and a rapid increase in clean energy application. In the context of China’s coal overcapacity, port and rail capacities are difficult to change in the short term. This study forecasts the monthly coal traffic of Daqin Railway on the basis of the seasonal autoregressive integrated moving-average Markov model and then uses the monthly coal transport data of this railway from September 2009 to November 2019 as samples for model training and verification. Coal traffic from December 2019 to September 2020 is accurately predicted. This study also analyzes the effects of China’s industrial structure adjustment, clean energy utilization, and low-carbon usage on the coal transport volume of Daqin Railway. In addition, the characteristics of seasonal fluctuation and the development trend of Daqin Railway’s coal traffic are explored. This study provides a reference for adjusting the train operation chart of Daqin Railway’s coal transport and developing a special coal train operation plan. It can determine the time of coal transport peak warning, improve the efficiency of coal transport management, and eventually realize a reasonable allocation of resources for Daqin Railway.

Traffic congestion has been an important problem all over the world. Advanced transportation management system (ATMS) that provides information for traffic control and management addresses this problem. McMaster Algorithm, one of the most classical congestion detection algorithms, has been widely used in practice. However, it still has some limitations such as difficulty of determining its parameters which are lower bound of uncongested data (LUD), critical occupancy (Ocrit) and critical volume (Vcrit). This paper transforms this problem to be an optimization problem and presents a new method that integrates gradient descent algorithm and particle swarm optimization algorithm (GPSO), with which parameters of McMaster Algorithm are extracted from the occupancy and volume of the detector. Thus it can determine the parameters of McMaster Algorithm more quickly and precisely. The parameters of McMaster algorithm are validated with real data from Yuwu Expressway in Chongqing. The results show that GPSO can help operators find the best parameters more effectively and make McMaster Algorithm yield a higher accuracy rate and recall rate.