Introduction: Due to the inefficiency and high cost of the current healthcare supply chain mode, in order to adapt to the great changes in the global economy and public health, it is urgent to choose an effective mode for sustainable development of healthcare supply chain. The aim of this paper is to use artificial intelligence systems to make intelligent decisions for healthcare supply chain mode selection.Methods: Firstly, according to the economic benefits, social benefits and environmental benefits of healthcare supply chain, this paper identifies different healthcare supply chain modes in combination with artificial intelligence technology. Secondly, this paper presents the intelligent choice optimization method of healthcare supply chain mode based on deep reinforcement learning algorithm. Finally, the effect of artificial intelligence in healthcare supply chain mode selection is verified by simulation experiment.Results and Discussion: The experimental results show that healthcare supply chain mode selected by artificial intelligence is basically consistent with the target mode, while healthcare supply chain mode selected by the basic selection method, BP neural network method and big data method is different from the target mode, which indicates that AI has more advantages in the selection of medical supply chain mode. Therefore, we recommend the application of artificial intelligence to healthcare supply chain management. This study not only makes up for the ineffective problems of existing methods, but also makes up for the gaps in the application of AI technology in the field of healthcare supply chain. The scientific value of this paper is that the proposed framework and the artificial intelligence algorithm enrich the relevant theories of healthcare supply chain research and provide methodological guidance for intelligent decision-making of healthcare supply chain. At the same time, for medical enterprises, this research provides a new practical guideline for the application of artificial intelligence in the sustainable development and modern management of healthcare supply chain.

Hiking is a form of green tourism which deserves promotion and popularization, especially in present day China. However, the risks inherent in hiking could have a negative impact on the development of hiking tourism. It is important to better understand how people perceive the risks of hiking and what type of experience attributes they prefer. However, no studies have investigated the nature of risk perception from the perspective of hikers. This study explores the dimensions of the perceived risk of hiking and investigates the associated factors of hiking risk perception as well as hiking preference. A questionnaire with 18 items was used to capture people’s perception of hiking risks, and two groups of samples were surveyed. Generally, this study identified two dimensions of perceived risk towards hiking based on a sample of hikers, i.e., physical risk and psychological risk. Demographic variables such as gender, upbringing background, and hiking frequency were shown to predict hiking risk perception while gender and hiking frequency predicted route preference. The personality trait of sensation seeking appeared to be a significant predictor of hiking preference. These findings lend themselves to market segmentation and marketing strategies on hiking tourism.

Considering economic, environmental, and social issues, the sustainability of the supply chain has drawn considerable attention due to societal and environmental changes within the supply chain network. The strategic study of the entire supply chain process and maximizing an organization’s competitive advantage depend heavily on supplier selection based on sustainable indicators. Selecting sustainable suppliers for the supply chain is challenging since it is a multi-criteria decision-making (MCDM) problem with significant uncertainty in the decision-making process. This study uses the VlseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) technique and single-valued neutrosophic sets (SVNS) to deal with the challenge of choosing a sustainable supplier with insufficient information. This method reduces the influence of personal experience and preference on the final evaluation results and the problem of excessive individual regret caused by factor correlation and improves the consistency of evaluation results. Finally, the method’s success and adaptability are demonstrated by sensitivity analysis and additional comparison analysis, and the benefits and drawbacks of the suggested framework are examined. Compared to other approaches, it can assist decision-makers in communicating fuzzy and uncertain information, offering a perspective and approach for MCDM in the face of such situations, and helping them select suppliers of high caliber and who practice sustainable business practices.

Supply chain resilience has garnered significant attention from both scholars and practitioners. However, the complex nature of the topic has resulted in a dearth of research on its key elements and formation mechanisms. To bridge this knowledge gap, we implemented grounded theory and conducted semi-structured interviews with 23 interviewees, which led to the identification of six key elements of supply chain resilience through open coding, axial coding, selective coding, and theoretical model saturation testing. These elements are product supply resilience, resource resilience, partner resilience, information response resilience, capital resilience, and knowledge resilience. Drawing from the key elements and the three phases of supply chain resilience (readiness, response, and recovery), we illustrated its formation mechanism and constructed a theoretical model of the influencing factors and pathways of supply chain resilience. We devised a questionnaire based on the coding results and confirmed its reasonableness and validity with a small sample of 109 questionnaires. Subsequently, a large sample of 409 questionnaires was used to test and validate the theoretical model using structural equation modeling, demonstrating that the identified key elements positively impact supply chain resilience. In sum, our paper enriches the comprehension of supply chain resilience by identifying its key elements and elaborating on its formation mechanism.

How to evaluate the resilience level and change trend of supply chain is an important research direction in current supply chain management practice. This paper proposes a new method of supply chain resilience assessment based on hesitant fuzzy linguistic term set (HFLTS) and matter element extension theory. Firstly, based on the research status quo at home and abroad, a low-carbon enterprise supply chain resilience assessment index system is established, which includes six first-level indicators and corresponding 21 second-level indicators of product supply resilience, resource resilience, partner resilience, information response resilience, financial resilience and knowledge resilience. Secondly, HFLTS was used to collect expert opinions and Ordered Weighted Arithmetic (OWA) to calculate the expert composite language, by which the fuzzy evaluation matrix of supply chain resilience assessment indicators was obtained. Once again, the resilience indicator weights are determined based on a game-theoretic portfolio assignment method combining the best-worst method (BWM) and the CRITIC method. Finally, the nearness degree function is combined with the extension comprehensive evaluation method to improve the matter element extension model, and the supply chain resilience assessment model of low-carbon enterprises based on the game theory combination assignment-improved matter element extension is established. Taking X low-carbon enterprise as an example, the evaluation results show that the supply chain resilience level of this enterprise is II, and the eigenvalue of the grade variable is 2.69, and the supply chain resilience is shifting to III, and the supply chain resilience is shifting to III, which indicates that the supply chain resilience of this enterprise is being enhanced. Therefore, the improved matter element extension not only ensures the accuracy of the evaluation results, but also has higher prediction accuracy.