Taking carbon emissions into account in decision-making on distribution network operations contributes to achieving the goal of promoting energy conservation and emissions reduction. The focus of this paper is to research multicapacity hierarchical location-routing robust optimization in distribution network design under carbon trading policies. First, this problem is described as a mixed integer nonlinear programming model. Then, based on strong duality theory, the nonlinear model is transformed into a linear robust equivalent model. Finally, GUROBI software is used for numerical calculation and analysis. The results suggest the following: carbon trading policies have a carbon abatement effect; with a decrease in the carbon emissions cap and an increase in carbon trading prices, carbon emissions undergo a ladder-like downward trend; uncertain fluctuations in freight units will influence the optimal decision-making patterns of enterprises; and making more vehicles available will reduce carbon emissions. The government should set a reasonable carbon emissions cap according to market conditions. Enterprises could adopt robust control parameters on the basis of their decision-making preferences and consider the impact of carbon trading policy in formulating and adjusting an optimal decision-making scheme.

With the rapid development of cloud computing, users are exposed to increasingly serious security threats such as data leakage and privacy exposure when using cloud platform services. Problems in data security, such as inaccurate screening of indicators, lack of scientific validation of reputation evaluation results are also existed. In order to solve the problems, based on cloud environment, a security reputation model using S-AlexNet convolutional neural network and dynamic game theory (SCNN-DGT) is proposed. And it is used to protect the privacy of health data in Internet of Things (IoT). Firstly, the text information of user health data is pre-classified by using S-AlexNet convolutional neural network. Then, a recommendation incentive strategy based on dynamic game theory is proposed. So that the reputation model of user health data security is built, and the evaluation system of the model is established. Finally, an experimental study is carried out to verify the validity of the model and the model index screening. It is shown by experimental results that the model can solve the problems of low reliability of health data screening index, and low accuracy of credit distinction in cloud environment. Therefore, the reliability of mobile terminals is improved, and data security and privacy protection of mobile cloud services are strengthened effectively.

In order to promote the sustainable and coordinated development of the logistics industry and the manufacturing industry in the Yangtze River Economic Belt of China and provide the policy makers with decision-making references, this paper explored the spatio-temporal evolution of the coupling coordination development level of the two industries. A three-stage super-efficiency SBM model, which eliminated the influence of environmental factors and random errors, was constructed to make it possible to conduct an in-depth comparative analysis on the effective decision-making units (DMUs), making the calculation results more accurate. This was the main contribution of this paper. Based on the new model considering undesirable output, this paper analyzed the panel data of 11 provinces and cities in the Yangtze River Economic Belt from 2007 to 2017 and investigated the coordination development level from the dimensions of time and space considering the energy input and carbon emissions of the two industries. Our main research findings were as follows. First, due to the relative lagging of the logistics industry in promoting the development of the manufacturing industry, the overall level of the coordination between the two industries was at a stage of limited coordination. Second, the regional differences were significant with a spatial evolution pattern of “high in the east and low in the west”. Third, environmental factors affected the input efficiency of the logistics industry and the manufacturing industry, especially the latter. Overall, this paper made theoretical and practical contributions to promoting the joint development of the two industries, improving the logistics industry and upgrading the manufacturing industry.

To improve the effectiveness of flood disaster relief operations, by ensuring timely and accurate delivery of urgently needed supplies to affected areas, this study focuses on the problem of emergency material distribution during floods. With the objective of minimizing the overall delivery time of emergency materials, we propose a coordinated optimization model that integrates trucks, speedboats, and drones for effective distribution of emergency supplies in flood‐affected areas. To solve this optimization problem, we introduce an improved adaptive large neighborhood search (IALNS) algorithm, which builds on the traditional ALNS framework through refined tuning of deletion and insertion operators. Comparative analyses are conducted with a genetic algorithm, simulated annealing algorithm, and tabu search algorithm. The results reveal that the average performance gap of IALNS compared to these methods is 91.13%, 152.72%, and 16.92%, respectively. The experimental results demonstrate that the efficiency of the proposed model and algorithm in addressing the emergency supply distribution problem during flood disasters, highlighting the superior performance of IALNS. This research contributes to enhancing disaster response strategies, ultimately leading to improved outcomes for flood‐affected communities.