Determining the optimal number of emergency medical suppliers for the government to contract with in the context of public health events poses a challenging problem. Having too many suppliers can result in increased costs, while having too few suppliers can potentially expose the government to supply risks. Striking the right balance between these two factors is crucial in ensuring efficient and reliable emergency response and management. This study examines the process of determining the appropriate number of suppliers in emergency medical supply chain. By incorporating option contracts and employing the total cost of government procurement as the objective function, the analysis focuses on the impact of relevant parameters on the optimal number of suppliers. Furthermore, the study investigates the optimal supplier quantities under different types of option contracts. The proposed decision model for determining the optimal number of suppliers in this paper considers three key factors: the supply risk associated with emergency medical supplies, the reserve cost of government procurement, and the responsiveness of emergency medical supplies. Additionally, a method is introduced for selecting the quantity of emergency medical suppliers based on flexible contracts. This approach offers a scientific foundation for the government to effectively address the challenge of supplier quantity selection when faced with risks related to shortages, expiration, and the combination of both.

Third-party logistics companies face a challenging task in minimizing inventory transportation costs due to the complexities of managing numerous suppliers. Effectively optimizing costs becomes a formidable problem for such companies. This empirical research has yielded strategies for minimizing the inventory transportation cost specifically for company D. Through a rigorous optimization process, the findings presented in this paper demonstrate an average reduction of 7.18% in company D's inventory transportation cost. By jointly optimizing inbound logistics inventory transportation under VMI-TPL mode, this study extends the theory of supplier managed inventory and improves the inbound logistics mode. The results of this study can provide quantitative support and decision-making references for the project operation management of company D and similar enterprises.

Accurately forecasting emergency material demand during the initial stages of disaster response is challenging due to communication disruptions and data scarcity. This study proposes a hybrid model integrating regression analysis and intelligent analysis to estimate casualties and predict emergency supply needs indirectly. A case study of five earthquake-affected villages validates the model, using building collapse rates and population data to calculate casualties and determine the demand for essential supplies, including food, water, medicine, and tents. The findings demonstrate that the proposed approach effectively addresses the “black box” condition by utilizing correction factors for population density, disaster preparedness, and emergency response capacity, providing a structured framework for rapid and accurate demand forecasting in disaster scenarios.

The ecological governance system has made significant progress in information technology construction, and digitization has become an important driving force in the construction of this system. This article delves into the digital construction path of ecosystem governance and elaborates on the practical application effectiveness of digitalization in the ecological governance system through two specific cases: intelligent water management and air pollution warning digital models. Furthermore, we adopt the TOE (Technology–Organization–Environment) model to integrate a collaborative theory with the concept of multi-center governance and innovatively propose a new model for nearshore ecological environment protection management. On this basis, we constructed an evaluation index system and established a theoretical model of the impact of environmental regulations on nearshore ecological efficiency. After empirical analysis, we found that the impact of environmental regulations on ecological efficiency in coastal areas presents a curvilinear characteristic. In the modern ecological governance system, organizational factors and technological factors are the two modules that have the greatest impact on system construction, accounting for 49% and 31% of the weight of the indicator system, respectively, followed by technological advantages and the urgency and feasibility of construction. Gender factors indicate that technology and organization are necessary factors that constrain institutional development.

In recent years, the escalation in emergency occurrences has underscored the pressing need for expedient responses in delivering essential supplies. Efficient integration and precise allocation of emergency resources under joint government–enterprise stockpiling models are pivotal for enhancing emergency response effectiveness and minimizing economic repercussions. However, current research predominantly focuses on contract coordination and cost-sharing within these joint reserve modes, overlooking significant discrepancies in emergency supply classification standards between government and enterprise sectors, as well as the asymmetry in cross-sectoral and cross-regional supply information. This oversight critically impedes the timeliness and accuracy of emergency supply responses. In practice, manual judgment has been used to match the same materials under differing classification standards between government and enterprise reserves. Still, this approach is inefficient and prone to high error rates. To mitigate these challenges, this study proposes a methodology leveraging the BERT pre-trained language model and TextCNN neural network to establish a robust mapping relationship between these classification criteria. The approach involves abstracting textual representations of both taxonomical classes, generating comparable sentence vectors via average pooling, and calculating cosine similarity scores to facilitate precise classification mapping. Illustrated with China’s Classification and Coding of Emergency Supplies standards and Global Product Classification standards, empirical validation on annotated data demonstrates the BERT-TextCNN model’s exceptional accuracy of 98.22%, surpassing other neural network methodologies such as BERT-CNN, BERT-RNN, BERT-BiLSTM, etc. This underscores the potential of advanced neural network techniques in enhancing emergency supply management across diverse sectors and regions.