Plenty of research focuses on supply chain finance and its risk, qualitatively or quantitatively. However, only a little literature studies on internet supply chain finance (ISCF), especially on its risk by quantitative analysis. After analyzing the information of partners’ panorama data and upstream and downstream data in the internet supply chain, the paper constructs a multiple dimensional intelligent risk assessment system for ISCF. By using the analytic hierarchy process and gray assessment theory, a risk assessment model of ISCF is built. Based on the collected big data, tracking and monitoring partners’ panoramic data and upstream and downstream data of the supply chain real time, the risk of ISCF can be calculated through the assessment model, so investor can decide whether to finance or not before lending and monitor the lender dynamically after loaning. Taking Zhong-ken Supply Chain Co., Ltd., a focal company in the supply chain, as an example, the paper evaluates the risk of lending to one financing enterprise and obtains a specific risk value, by which to describe the risk degree. Therefore, the model has certain practicability.

Research on the treatment center allocation, technology adoption and vehicle routing problem of the hazardous waste under the uncertain condition that waste generation amount is fuzzy. Based on the existing literature, considering the diversity of wastes type and technology, waste-technology compatibility and designing a measure method of risk equality, taking the risk, cost minimization and risk equality maximization as optimum objectives, a multi-objective model with triangular fuzzy number was proposed. Transforming the fuzzy waste generation constraints by considering triangular fuzzy number's characteristics, the original fuzzy multi-objective model was transformed into the general multi-objective 0–1 mixed-integer linear programming model. The algorithm was designed to solve this model by adopting fuzzy technique. Finally, an example was experimental analyzed to demonstrate the practical feasibility of the model and algorithm.