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.

With the development of society and the needs of consumers, the concept of low-carbon and the improvement of customer satisfaction have become the focus of enterprises. Cold chain logistics is a product with high energy consumption and perishable characteristics, this paper will consider improving customer satisfaction on the basis of low carbon, and establish a cold chain distribution center location-routing optimization model with the minimum total cost as the optimization objective. The mathematical model is constructed and solved by using bi level programming theory and genetic algorithm, and neighborhood search is added to the lower model to optimize the route structure. Finally, the simulation results show that the final cost of considering carbon emissions and customer satisfaction is less than the cost of only considering the shortest route.

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.