With the development of the economy, the prediction of expressway project costs has gained increasing research attention. In this study, based on the convolution neural network (CNN) algorithm, the prediction of the expressway construction cost was analyzed with respect to the conceptual design stage. By summarizing the existing research results, 10 new factors related to the bridge and tunnel are creatively introduced into the cost-prediction index of the expressway conceptual design stage. In addition, the data structure of the expressway project cost prediction is defined and a CNN model is established. Finally, the project information of 415 expressways in China collected in this study is used to verify the research results. The results of the case analysis show that the 10 new indexes related to the bridge and tunnel can improve the prediction accuracy of the model. In addition, the CNN model is more suitable for solving the high-dimensional nonlinear problem of expressway cost prediction than the conventional artificial-neural-network and regression-analysis models, and it can improve the prediction accuracy. The findings of this study can be used to devise financial plans in the early stage of expressway construction and facilitate cost management at the conceptual design stage to help investors acquire project funds in advance.

Reasonable maintenance plans are important for ensuring safe train operation and prolonging the service life of tracks. However, previous studies on the scheduling and optimization of railway track maintenance plans possess the following limitations. First, scheduling optimization models generally operate at the planning level with months as the time units rather than at the operation scheduling level with days as the time units. Second, they fail to consider the bidirectional feedback and dynamic impact between predicted condition and maintenance scheduling. Third, actual on-site operation strategies (e.g., opportunistic and centralized maintenance) are neglected. To address these issues, this paper proposes a novel operation-level maintenance scheduling framework based on segment condition considering both opportunistic and centralized maintenance strategies. According to the problem characteristics, ae hybrid multi-objective optimization algorithm based on a quantum-behaved particle swarm optimization was designed. Furthermore, by utilizing actual practical cases, the effectiveness of the model and algorithm was verified via comparison with MOPSO, NSGA-II, NSGA-III, and Gurobi in different scenarios.

In recent years, the energy consumption of vehicles has gained widespread attention due to the increasing importance of energy and environmental issues. Coupled with the explosive demand for vaccines that has spawned the massive deployment of refrigerated trucks, energy savings and efficiency improvement are the goals pursued by pharmaceutical logistics companies while getting the vaccine distribution task done. In order to evaluate the fuel consumption of refrigerated trucks during vaccine distribution, in this paper, we construct a mathematical model for the vehicle routing problem with time windows (VRPTW) for vaccine distribution with the aim of minimizing the total cost, including fossil fuel cost and penalty cost. Due to the NP-hardness and nonlinearity of the model, a genetic algorithm with a large neighborhood search operator (GA-LNS) and TSP-split encoding method is customized to address the large-scale problem. Numerical experiments show that the algorithm can obtain a near-optimal solution in an acceptable computational time. In addition, the proposed algorithm is implemented to evaluate a case of vaccine distribution in Haidian, Beijing, China. Insights on the effects of seasonal temperature, vehicle speed, driver working hours, and refrigeration efficiency are also presented.

The linear scheduling method (LSM) for optimization in linear projects has been the focus of numerous academic studies over the years. However, research on incorporating reverse construction activities and other practical scenarios, such as flexible activity–section–crew assignment demands, in linear project-scheduling optimization remains insufficient. This has further spurred research on practical scenario-based linear project-scheduling optimization. We performed an analysis of a description method system within the LSM framework, outlining the spatiotemporal logical relationship in scenarios involving reverse activities. A scheduling optimization model incorporating a flexible constraint system, capable of handling multisection–multicrew, multicrew–multimode, and multicrew–multiconstruction direction scenarios is developed to enhance the practicability of linear project-scheduling optimization. Additionally, an advanced grey wolf optimization (GWO) algorithm is devised and validated through a real-world case study. The case study provides insights into the impact of reverse construction activities on the linear project-scheduling problem, specifically in the dimension of project duration.

Predicting the tamping cycles of railway track sections based on track geometry deterioration rules is necessary to reasonably allocate the limited tamping maintenance resources. Existing research on track geometry deterioration modeling for tamping cycle prediction lacks simultaneous consideration of the deterioration characteristics including heterogeneity, uncertainty, and historical dependence, thereby limiting the accuracy of the prediction results. Thus, this study considers a 200 m track segment as the basic object and uses the power-time-transformed Wiener process to develop a deterioration prediction model for the longitudinal level of a segment between two adjacent tamping operations. Moreover, it individually estimates the model parameters for each track segment to predict the tamping maintenance cycle for each segment combined with the tamping maintenance threshold of the longitudinal level index. Finally, through a case study of the Chinese Lanxin Railway line, the effectiveness of the proposed model and different parameter estimation methods is assessed.