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 the logistics industry modernizes, living standards improve, and consumption patterns shift, the demand for fresh food continues to grow, making cold chain logistics for perishable goods a critical component in ensuring food quality and safety. However, the presence of both soft and hard time windows among demand nodes can complicate the single-network distribution of perishable goods. In response to these challenges, this paper proposes an optimization model for multi-distribution center perishable goods delivery, considering both one-echelon and two-echelon network joint distributions. The model aims to minimize total costs, including transportation, fixed, refrigeration, goods damage, and penalty costs, while measuring customer satisfaction by the start time of service at each demand node. A two-stage heuristic algorithm is designed to solve the model. In the first stage, an initial solution is constructed using a greedy approach based on the principles of the k-medoids clustering algorithm, which considers both spatial and temporal distances. In the second stage, the initial routing solution is optimized using a linear programming approach from the Ortools solver combined with an Improved Adaptive Large Neighborhood Search (IALNS) algorithm. The effectiveness of the proposed model and algorithm is validated through a case study analysis. The results demonstrate that the initial solutions obtained through the k-medoids clustering algorithm based on spatio-temporal distance improved the overall cost optimization by 1.85% and 4.74% compared to the other two algorithms. Among the three two-stage heuristic algorithms, the Ortools-IALNS proposed here showed enhancements in the overall cost optimization over the IALNS, with improvements of 3.24%, 1.12%, and 0.41%, respectively. The two-stage heuristic algorithm designed in this study also converged faster than the other two heuristic algorithms, with overall optimization improvements of 1.55% and 1.28%, further validating the superior performance of the proposed heuristic algorithm.

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 logistics industry modernizes, living standards improve, and consumption patterns shift, the demand for fresh food continues to grow, making cold chain logistics for perishable goods a critical component in ensuring food quality and safety. However, the presence of both soft and hard time windows among demand nodes can complicate single-network distribution of perishable goods. In response to these challenges, this paper proposes an optimization model for multi-distribution center perishable goods delivery, considering both one-echelon and two-echelon network joint distribution. The model aims to minimize total costs, including transportation, fixed, refrigeration, goods damage, and penalty costs, all while measuring customer satisfaction by the start time of service at each demand node. A two-stage heuristic algorithm is designed to solve the model. In the first stage, an initial solution is constructed using a greedy approach, based on the principles of the k-medoids clustering algorithm that considers both spatial and temporal distances. In the second stage, the initial routing solution is optimized using a linear programming approach from the Ortools solver combined with an Improved Adaptive Large Neighborhood Search (IALNS) algorithm. The effectiveness of the proposed model and algorithm is validated through a case study analysis. Results indicate that the enhanced algorithm achieves faster convergence and more effective overall cost optimization.