Modeling a sustainable vaccine supply chain for a healthcare system

Optimization Modeling for Pandemic Vaccine Supply Chain Management: A Review and Future Research Opportunities

During various stages of the COVID-19 pandemic, countries implemented diverse vaccine management approaches, influenced by variations in infrastructure and socio-economic conditions. This article provides a comprehensive overview of optimization models developed by the research community throughout the COVID-19 era, aimed at enhancing vaccine distribution and establishing a standardized framework for future pandemic preparedness. These models address critical issues such as site selection, inventory management, allocation strategies, distribution logistics, and route optimization encountered during the COVID-19 crisis. A unified framework is employed to describe the models, emphasizing their integration with epidemiological models to facilitate a holistic understanding. This article also summarizes evolving nature of literature, relevant research gaps, and authors' perspectives for model selection. Finally, future research scopes are detailed both in the context of modeling and solutions approaches.

Intelligent selection of healthcare supply chain mode - an applied research based on artificial intelligence

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.

Designing a sustainable reverse supply chain network for COVID-19 vaccine waste under uncertainty

The vast nationwide COVID-19 vaccination programs are implemented in many countries worldwide. Mass vaccination is causing a rapid increase in infectious and non-infectious vaccine wastes, potentially posing a severe threat if there is no well-organized management plan. This paper develops a mixed-integer mathematical programming model to design a COVID-19 vaccine waste reverse supply chain (CVWRSC) for the first time. The presented problem is based on minimizing the system's total cost and carbon emission. The uncertainty in the tendency rate of vaccination is considered, and a robust optimization approach is used to deal with it, where an interactive fuzzy approach converts the model into a single objective problem. Additionally, a Lagrangian relaxation (LR) algorithm is utilized to deal with the computational difficulty of the large-scale CVWRSC network. The model's practicality is investigated by solving a real-life case study. The results show the gain of the developed integrated network, where the presented framework performs better than the disintegrated vaccine and waste supply chain models. According to the results, vaccination operations and transportation of non-infectious wastes are responsible for a large portion of total cost and emission, respectively. Autoclaving technology plays a vital role in treating infectious wastes. Moreover, the sensitivity analyses demonstrate that the vaccination tendency rate significantly impacts both objective functions. The case study results prove the model's robustness under different realization scenarios, where the average objective function of the robust model is less than the deterministic model ones' in all scenarios. Finally, some insights are given based on the obtained results.