The development of multimodal transport has had a significant impact on China’s transportation industry. Due to the variability of the market environment, in this study, based on the context of the official launch of the national carbon emission trading market, the uncertainty of the demand and the randomness of carbon trading prices were considered. Taking minimum total transportation cost as the objective function, a robust stochastic optimization model of container multimodal transport was constructed, and a hybrid fireworks algorithm with gravitational search operator (FAGSO) was designed to solve and verify the effectiveness of the algorithm. Using a 35-node multimodal transportation network as an example, the multimodal transportation costs and schemes under three different modes were compared and analyzed, and the influence of parameter uncertainty was determined. The results show that the randomness of carbon trading prices will lead to an increase or decrease in the total transport cost, while robust optimization with uncertain demand will be affected by the regret value constraint, resulting in an increase in the total transport cost. Multimodal carriers can reduce transportation costs, reduce carbon emissions, and improve the transportation efficiency of multimodal transportation by comprehensively weighing the randomness of carbon trading prices, the nondeterminism of demand, and the relationship between the selection of maximum regret values and transportation costs.

Supply Chain Finance (SCF) is a new type of financing business carried out by commercial banks on the basis of supply chain management, which effectively promotes the healthy development of the supply chain. As the most typical mode of SCF, accounts receivable financing mode can use the part of accounts receivable occupying working capital for financing, which is widely used. In order to effectively manage the credit risk in the Supply Chain Finance and maintain the healthy operation of the supply chain, this paper proposes a supply chain financial credit risk prediction model based on PCA-GA-SVM. First, principal component analysis (PCA) is used to reduce the dimension of the original index system, and then genetic algorithm (GA) is used to optimize the parameters of support vector machine (SVM). Finally, the principal components selected by PCA are input into the GA-SVM model for training, and the final prediction model is established. The running results show that the prediction performance of PCA-GA-SVM model is better than that of SVM and GA-SVM models. It has a good generalization ability, which can be used as a reference for commercial banks to improve the credit risk management ability of Supply Chain Finance and is conducive to the sustainable development of supply chain finance business.

In this study, a bi-objective optimization model was established to solve the cooperative distribution problem of a multi-center hybrid fleet by integrating reverse logistics under real-time road conditions. According to the characteristics of the problem and considering the power level and battery capacity of electric vehicles, the multi-objective immune genetic algorithm (MOIGA) was designed and compared with an elitist strategy genetic algorithm, i.e., the fast non-dominated sorting genetic algorithm (NSGA-II). The scale of the MOIGA solution set exceeded that of the NSGA-II, which proved that the global search ability of MOIGA was better than that of the NSGA-II. The operating efficiency of the MOIGA was lower than that of the NSGA-II, but it could also find the optimal solution within an acceptable time range. This method can reduce the total cost of operating a hybrid fleet and can meet the needs of customers, and therefore, improve customer satisfaction.

Express delivery in rural areas of China has many problems, such as high delivery cost and low efficiency. As an effective way to solve the difficulties of rural delivery, it is important to study the innovation and application of a joint distribution model. In the background, this paper takes express delivery enterprises in rural areas as the research object. First, it proposes to construct a three-level “county-town-village” joint distribution system in which e-commerce platforms participate. Next, it establishes an evolutionary game model of express delivery enterprise joint distribution alliance and solves it. Finally, the model is analyzed through numerical simulation. The results show that the distribution system of express delivery enterprises in rural areas is affected by excess returns, early input costs, operating costs, cooperation risks, penalty costs, learning and absorption capacity of enterprises and other factors. After introducing the rewards and punishments of e-commerce platforms as an independent influencing factor in the evolutionary game model, the shorter time for express companies to finally make cooperation strategies indicates that the rewards and punishments of e-commerce platforms have a positive significance in promoting the rapid development and stable operation of a rural logistics joint distribution system.

Water inrush is a serious geological disaster in deep coal mining and rock engineering in China. The occurrence of water inrush is affected by the interaction of many controlling factors in underground reservoir areas. In order to improve the accuracy of water inrush risk evaluation, the changes in index weights due to the change in influencing factors and the uncertain factors should be both considered. In this study, an evaluation model that integrates variable weight and unascertained measure (VWUM) theories was established. First, a modified credible degree recognition was established to make the evaluation results represent both the evaluation grade and the difference in the degree to which the indexes belong to the same grade. Then, the VWUM model was established by coupling the variable weight, unascertained measure, modified credible degree recognition, and geographic information system (GIS). Last, the VWUM model is applied to a case study to evaluate water inrush risk. Results indicates that more detailed risk grades and their spatial distribution zones can be obtained by the VWUM model compared with those obtained by the water inrush coefficient. The VWUM model can provide a more accurate evaluation result.

Real-time order arrivals and stringent timeliness demands in e-commerce pose significant challenges to production-distribution coordinated scheduling in distributed manufacturing systems. This paper tackles the integrated production and distribution scheduling problem within a dynamic distributed hybrid flow shop. We formulate a mixed-integer linear programming model aimed at minimizing average order tardiness and total operational cost. To address dynamic uncertainties, we propose an improved decomposition-based multi-objective evolutionary algorithm (I-MOEA/D) operating within a rolling horizon framework. The algorithm integrates three critical components: an urgency-based emergency window management strategy that caps computational complexity, a 2-opt local search operator that boosts vehicle routing efficiency, and acceleration techniques—including order dictionary indexing, lightweight object replication, and distance caching—that guarantee real-time responsiveness. The window size is determined as W_max = 50 through sensitivity analysis. Extensive experiments conducted on 100, 200, and 300-order scenarios with 30 independent random seeds demonstrate that I-MOEA/D markedly outperforms NSGA-II, MOALNS, and a reinforcement learning-driven hyper-heuristic (RL-HH). For 200 orders, I-MOEA/D reduces average tardiness by 18.5%, 41.0%, and 58.7% compared to NSGA-II, MOALNS, and RL-HH, respectively, while maintaining competitive cost performance (a cost gap of 16.99% relative to the static lower bound). The IGD metric achieves 0.0031, confirming good convergence and diversity. Acceleration techniques cut computation time by 56% without sacrificing solution quality. The algorithm scales efficiently with problem size. These results validate that I-MOEA/D delivers effective real-time decision support for dynamic distributed production-distribution systems.