COVID-19 vaccine distribution planning using a congested queuing system-A real case from Australia

Area of Science:

• Operations Research
• Public Health
• Supply Chain Management

Background:

• The COVID-19 pandemic highlighted the critical need for effective crisis-induced vaccine supply chain management.
• Immediate responses are crucial for coordinating and distributing vaccines during public health emergencies.

Purpose of the Study:

• To develop a multi-period queuing model for crisis-induced vaccine supply chains.
• To minimize vaccinee wait times and total vaccine inventory costs simultaneously.
• To analyze the impact of vaccine inventory, efficiency, and risk aversion on arrival rates.

Main Methods:

• Development of a bi-objective non-linear programming model.
• Application of the goal attainment algorithm and the non-dominated sorting genetic algorithm II (NSGA-II).
• Implementation of solution repairs to enhance NSGA-II efficiency and performance evaluation using standard metrics.

Main Results:

• The improved NSGA-II algorithm demonstrated superior performance in numerical examples, validated by Friedman and Wilcoxon signed-rank tests.
• The model was applied to an authentic case study in Australia, generating insights for efficient vaccination programs.
• Analysis confirmed the model's ability to balance competing objectives of minimizing wait times and inventory costs.

Conclusions:

• The proposed queuing model and enhanced NSGA-II algorithm provide a robust framework for optimizing vaccine distribution during crises.
• The study offers valuable insights for policymakers and supply chain managers to design more efficient and responsive vaccination programs.
• Effective management of vaccine supply chains is essential for mitigating the impact of future health crises.