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Remote Laboratory Management: Respiratory Virus Diagnostics
Published on: April 6, 2019
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Testing facility location and dynamic capacity planning for pandemics with demand uncertainty.
Kanglin Liu1, Changchun Liu2, Xi Xiang3
1School of Traffic and Transportation, Beijing Jiaotong University, Beijing, 100044, China.
European Journal of Operational Research
|December 1, 2021
Summary
This study introduces a two-phase optimization framework for locating pandemic testing facilities and managing supply chain capacity. The adaptive policy effectively meets fluctuating demand, achieving near-optimal results with limited predictions.
Area of Science:
- Operations Research
- Public Health Preparedness
- Epidemic Management
Background:
- The COVID-19 pandemic highlighted the need for robust supply chain and facility location strategies during public health emergencies.
- Effective control of infectious diseases relies on strategically placed critical facilities like testing and vaccination sites.
Purpose of the Study:
- To develop and evaluate a two-phase optimization framework for locating testing facilities and dynamically adjusting their capacity to meet pandemic-driven demand.
- To address the challenge of fluctuating demand for resources such as test kits during large-scale emergencies.
Main Methods:
- A two-phase optimization framework was proposed, utilizing sample average approximation for initial facility prepositioning.
- An online convex optimization-based Lagrangian relaxation approach, incorporating an online gradient descent algorithm, was developed to solve the first phase.
- Dynamic and adaptive dynamic allocation policies were designed for the second phase to manage capacity with short-term demand predictions.
Main Results:
- The proposed two-phase framework demonstrated effectiveness in meeting variable demand during pandemic scenarios.
- The adaptive dynamic allocation policy achieved a solution with only a 3.3% gap from the optimal solution, even with just one day's demand prediction.
- Numerical results validated the framework's ability to optimize facility location and capacity adjustments for emergency preparedness.
Conclusions:
- The developed optimization framework provides a viable solution for enhancing public health emergency response through strategic facility management.
- Dynamic capacity adjustment policies are crucial for efficiently responding to unpredictable demand fluctuations during pandemics.
- The research offers valuable insights for policymakers and public health officials in planning and resource allocation during health crises.
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