Background: The rapid growth of the elderly population poses a huge challenge for people to access medical services. The key to get rid of the dilemma is for patients to go firstly to primary medical institutions. Existing studies have identified numerous factors that can affect patients’ health institution choice. However, we currently know little about the role of Internet use in the patients’ medical decisions. The objective of this study is to explore health-seeking behavior and institution choice under the background of the Internet era from the perspective of older adults, and to analyze whether the Internet could guide patients to the appropriate medical institution so as to accomplish hierarchical treatment. Methods: The dataset comprises 9416 people aged 45 or above from the China Health and Retirement Longitudinal Survey (CHARLS), which, through multistage cluster sampling, was conducted in 2011, 2013, and 2015. Logistic regression, PSM, and FE model are used to estimate the influence of Internet use on the health care decision-making behavior. Results: Internet use has a significant positive impact on the self-treatment of common diseases (β = 0.05, p < 0.05). In terms of medical institution choices, those who use Internet are more inclined to choose top-level hospitals than community health service institutions to treat common diseases (β = 0.06, p < 0.01). Conclusions: The Internet has lowered the obstacles to learning about common ailments, resulting in a substitution impact of self-treatment for hospital care. However, Internet use may aggravate older adults’ perception of the risk of disease, which exacerbates the tendency of going to higher-level medical institutions for medical treatment. The finding of the study is useful for further rational planning and utilization of the Internet in order to guide patients to appropriate medical institution, which helps to improve the efficiency of the overall medical and health services.

In the context of economic globalization and the development of information networks, container liner transportation plays a crucial role in international trade. However, the inherent inflexibility of fixed schedules in liner operations poses challenges to the decarbonization of shipping and the stability of liner networks. Therefore, this paper focuses on the impact of port disruptions on route operations, develops a mixed integer nonlinear programming model considering fuel costs, recovery costs, and carbon emissions, and designs a hybrid evolutionary algorithm to solve the proposed model. The research findings indicate that scheduling strategies based on increased vessel speed, the adjustment of port calling sequences, and transshipment leasing after port skipping can effectively reduce the recovery costs after disruption events while meeting freight demand. When a disruption duration is less than 96 h, acceleration strategies and the adjustment of the port calling sequence are favorable choices. When the disruption duration exceeds 96 h, transshipment leasing after port skipping is a feasible solution to ensure the on-time delivery of cargo. The shifting of disrupted port position restricts the selection of scheduling strategies, particularly for ports located at the intersections of routes, which incur higher recovery costs. The implementation of carbon taxes affects the overall operating costs of liner companies, and an appropriate carbon tax level can constrain carbon emissions and ensure the sustainable development of the shipping industry.

Port congestion significantly impacts the reliability of container ship schedules. However, the existing research often treats vessel time in port as a random variable, failing to systematically consider the complex impact of port congestion on ship schedules. This study addresses the issue of container ship schedule design under port congestion. Vessel waiting times in ports are predicted and quantified by queueing theory, along with information on vessel schedules, cargo handling volumes, and available port operating time windows. We propose a mechanism for selecting container handling efficiencies for arriving vessels, thereby determining their in-port handling times. By jointly considering the uncertainty of vessel waiting and handling times in port, we establish a mixed-integer nonlinear programming model aimed at minimizing the total cost of liner transportation services. We linearize the model and solve it using CPLEX, ultimately devising a robust ship schedule. A simulation analysis is conducted on a real liner shipping route from Asia to the Mediterranean, revealing that extreme weather events, geopolitical conflicts, and other factors can lead to severe congestion at certain ports, necessitating timely adjustments to vessel schedules by shipping companies. Moreover, such events can impact the marine fuel market, prompting shipping companies to adopt strategies such as increasing vessel numbers and reducing vessel speeds in response to high fuel prices. Additionally, the container handling efficiency selection mechanism based on information sharing enables shipping companies to flexibly design liner schedules, balancing the economic costs and service reliability of container liner transportation.