Green innovation has emerged as a key catalyst for the sustainable growth of logistics enterprises. Green innovation not only helps logistics enterprises reduce operating costs but also enhances their competitiveness and promotes the entire industry’s transformation towards environmental protection and efficiency. However, logistics enterprises encounter technical bottlenecks, capital shortages, and insufficient talent and infrastructure when implementing green innovation. Collaboration between universities and industries serves as a crucial method for logistics companies to access external resources and plays a significant role in promoting technological progress, knowledge transfer, and innovation capability enhancement of enterprises. This research, grounded in the theories of social capital and dynamic capabilities, explores the mechanism from the perspective of resources and capabilities, and examines how university–industry collaboration affects green innovation. This research employs a hierarchical regression model to evaluate the proposed hypotheses. The research results show that university–industry collaboration has a positive impact on social capital, slack resources, and dynamic capabilities, and social capital, slack resources, and dynamic capabilities positively influence green innovation. The research results have certain reference value for logistics enterprises to promote green innovation.

As a consequence of e-commerce development, large quantities of returned goods are shipped every day. The vehicle routing problem with simultaneous delivery and pickup (VRPSDP) has become one of the most important areas of logistics management. Most related studies are aimed at minimizing travel time. However, the total number of collected goods is also very important to logistics companies. Thus, only considering the traveling time cannot reflect actual practice. To effectively optimize these operations for logistics companies, this paper introduces the vehicle routing problem with simultaneous pickup and delivery considering the total number of collected goods. Based on the principles of considering the number of collected goods, a bi-objective vehicle routing model minimizing the total travel time and maximizing the total number of collected goods simultaneously is developed. A polynomial time approximation algorithm based on the $\epsilon$-constraint method is designed to address this problem, and the approximation ratio of the algorithm is analyzed. Finally, the validity and feasibility of the proposed model and algorithm are verified by test examples, and several managerial insights are derived from the sensitivity analysis.

The volume of medical waste is continuously increasing, making its efficient collection crucial for the operational continuity of healthcare institutions and public health safety. According to World Health Organization regulations, medical waste is classified as either infectious or non-infectious based on its potential to pose infection risks. Accidents during the collection of infectious medical waste may lead to the spread of pathogens through air, water, and soil, thereby threatening public health and causing long-term environmental pollution. To mitigate infection risks and control costs in medical waste collection, this paper investigates a route optimization problem involving vehicles that collect both infectious and non-infectious medical waste simultaneously. A bi-objective route optimization method is proposed to balance infection risk control with cost minimization. This study formulates a bi-objective mixed-integer programming model aimed at minimizing both the total cost and the maximum risk, which is solved using an improved multi-objective particle swarm optimization (MOPSO) algorithm. The proposed model and algorithm are validated using Solomon benchmark instances. Furthermore, sensitivity analyses are conducted to examine the impact of key parameters, including the number of available vehicles and the ratio of infectious to non-infectious medical institutions, on route optimization outcomes. A comparative analysis with other algorithms confirms the superior performance of the improved MOPSO algorithm. The findings of this study provide theoretical support for decision-making in medical waste collection route optimization.

As the market of perishable goods in China continues to expand, reducing quality loss during transportation has become an urgent issue for the industry. Multimodal transport, as a key approach to optimizing the transportation structure and lowering logistics costs, has been increasingly adopted in practice. However, multimodal transport involves multiple transfers, and continuous vibration from transportation equipment throughout the transport process, together with impacts during transfer operations, can easily increase the loss of perishable goods, making it highly significant in practice to consider vibration loss during transportation in route planning. Since different transportation equipment generates different levels of vibration acceleration, this study considers the vibration losses caused by road, rail, and air transport. A bi-objective route optimization model for fresh produce multimodal transport is established, aiming to minimize total cost while maximizing product quality satisfaction. A hybrid algorithm combining an improved Strength Pareto Evolutionary Algorithm and a multi-objective adaptive large neighborhood search algorithm is designed to solve the model. The effectiveness of the model and algorithm is verified through case analysis, followed by sensitivity analysis on different time-sensitive factors of vibration damage and vibration acceleration. The results show that, compared with the multi-objective adaptive large neighborhood search algorithm and the non-dominated sorting genetic algorithm, the proposed algorithm can obtain solutions with lower total costs or higher product quality satisfaction. The minimum total cost of the multimodal transport scheme obtained by the proposed algorithm is reduced by 0.27% and 2.4%, respectively, compared with the multi-objective adaptive large neighborhood search algorithm and the non-dominated sorting genetic algorithm, while the maximum satisfaction is increased by 2.1% and 0.35%, respectively.