As a by-product of industrial development, large quantities of hazardous materials are shipped in the transportation network every day. The risk of transporting hazardous materials is an important consideration in transportation planning. Vehicle routing models in previous studies simultaneously minimized the transportation cost and the total risk. However, simply evaluating the total risk of a fleet as a whole does not consider each individual vehicle's risk. Even if the total risk is low, the risk of a specific vehicle may still be very high. In addition, the situation of several vehicles of a logistics company shipping hazardous materials and travelling on the same road together, called vehicles travelling in echelon, may cause a chain of incidents. This situation has been ignored in previous studies. In this paper, a bi-objective vehicle routing model for hazardous materials transportation with no vehicles travelling in echelon is developed, simultaneously minimizing the maximum risk of each vehicle and the transportation cost. A two-stage exact algorithm is developed based on the ϵ-constraint method with several improvements to solve the proposed problem. An approximation approach is proposed for this two-stage algorithm for large-scale problems. Furthermore, the approximation ratio and time complexity of this approximation algorithm are analyzed. Computational experiments with randomly generated instances are reported, and several managerial insights are derived from the sensitivity analysis.

As a consequence of industrial development, large quantities of hazardous materials are shipped everyday. Most related studies consider the risks as fixed weights on edges. However, the loads of the vehicles are important factors about the risks. Thus, considering the risks as fixed parameters cannot reflect actual practice. In addition, the vehicle routing models in previous studies simultaneously minimize the total transportation cost and the total risk. However, simply considering the total risk of a fleet as a whole cannot evaluate each vehicle's risk. Although the total risk is low, the risk of a specific vehicle may still be very high. In this paper, the actual load dependent risk is introduced, and a corresponding bi-objective vehicle routing model for hazardous materials transportation is developed, minimizing the maximum risk of vehicles and the total transportation cost simultaneously. An exact algorithm is developed based on the ε-constraint method with several improvements. For large-scale problems, an approximation approach is proposed. Computational experiments on a series of instances are reported and several managerial insights are derived from the sensitivity analysis.

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.

The efficiency of the same vehicle can vary in different regions, posing unique challenges and implications for electric vehicles (EVs) and plug-in hybrid electric vehicles (PHEVs) within a country. However, most studies have regarded countries as single entities, and it is difficult to assess differences in efficiency between similar entities by simply using the life cycle analysis (LCA) method. To provide the specific environmental efficiency of vehicles in each region, in this study, we used data from 100 cities in 30 provinces in China (4 provinces are not discussed due to a lack of data) and constructed a new road congestion indicator that simulated different road conditions at different times and in different regions. A more effective method, which consisted of LCA, two-stage data envelopment analysis (DEA) and a slack-based model (SBM), was integrated to reflect the phases of LCA more clearly. The results show that the well-to-wheel (WTW) emission range of internal combustion engine vehicles (ICEVs) is 288.28–217.40 CO₂-eq g/km, while it is 248.20–26.67 CO₂-eq g/km for EVs, which means the WTW carbon emissions of EVs are generally lower than those of ICEVs (except in Heilongjiang Province). Furthermore, it was concluded that provinces with a high proportion of hydropower and a high degree of power autonomy could adjust the proportion of thermal power and inter-provincial power transmission to enhance environmental sustainability, and this would not change provincial environmental efficiency. The analysis suggests that provinces should consider both environmental protection and electricity sustainability when planning their own power development, rather than only focusing on improving environmental efficiency.

The effective disposal of healthcare waste is a highly concerned issue. Healthcare waste collection poses transport risks and temporary storing risks, and the deliberation undertaken by decision-makers concerning waste collection entails potential scenario wherein some hospitals have not yet submitted their service requests and may submit their requests during the healthcare waste collection procedure. A routing optimisation problem for healthcare waste collection with temporary storing risks and sequential uncertain service requests is introduced. A two-stage decision-making is proposed and mathematical models corresponding to each stage are developed. Different solution algorithms are developed for different stages or different scales of instances, including the improved ε -constraint method and Non-dominated Sorting Genetic Algorithm-II for the solution procedure in Stage 1, and the Compare-choose-move algorithm for the solution procedure in Stage 2. Finally, the models and algorithms are tested by numerical instances and several suggestions for healthcare waste collection have been proposed based on sensitivity analysis.