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A two-stage robust optimization model for emergency service facilities location-allocation problem under demand
Hongyan Li1,2, Dongmei Yu3,4,5, Yiming Zhang1,2
1School of Business Administration, Liaoning Technical University, Huludao, 125105, People's Republic of China.
Scientific Reports
|January 22, 2025
Summary
This study optimizes emergency service facility (ESF) layout and supply allocation under demand uncertainty. It uses a robust optimization model to minimize costs, improving disaster response timeliness and sustainability.
Area of Science:
- Operations Research
- Disaster Management
- Environmental Science
Background:
- Effective emergency response relies on optimal emergency service facility (ESF) layout and resource allocation.
- Frequent emergencies necessitate robust pre-planning to address uncertainties and improve response timeliness.
Purpose of the Study:
- To develop a robust optimization model for emergency service facility network design under demand uncertainty.
- To integrate preparedness, deprivation, and environmental impact costs for comprehensive decision-making.
Main Methods:
- Formulation of a two-stage robust optimization model with a generalized budget uncertainty set.
- Utilization of the Column and Constraint Generation (C&CG) algorithm for solving the model.
- Case study application to the COVID-19 epidemic in Wuhan.
Main Results:
- The model effectively optimizes ESF deployment, emergency supply distribution, and sustainable measures.
- Analysis reveals the significant influence of demand uncertainty and environmental costs on optimal solutions.
- The C&CG algorithm demonstrates effectiveness in solving the complex robust optimization problem.
Conclusions:
- Robust optimization provides a valuable framework for enhancing emergency response systems.
- Balancing economic and environmental factors is crucial for sustainable disaster management.
- The proposed model offers practical insights for improving preparedness and response strategies.
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