An Improved Genetic Algorithm for Location Allocation Problem with Grey Theory in Public Health Emergencies

Area of Science:

• Operations Research
• Public Health
• Computer Science

Background:

• The COVID-19 pandemic significantly increased demand for emergency medical facilities (EMFs).
• Effective patient allocation to EMFs is critical during public health emergencies.
• Existing location-allocation problem (LAP) models require optimization for disaster scenarios.

Purpose of the Study:

• To develop an optimized location-allocation problem (LAP) model for emergency medical facilities (EMFs).
• To predict COVID-19 case numbers and estimate EMF demand using a grey forecasting model.
• To introduce a novel serial-number-coded genetic algorithm (SNCGA) for improved LAP solutions.

Main Methods:

• A grey forecasting model was used to predict cumulative COVID-19 cases and determine EMF demand.
• A serial-number-coded genetic algorithm (SNCGA) was developed and implemented in MATLAB.
• The emergency medical facility LAP (EMFLAP) model was solved using both SNCGA and the simple genetic algorithm (SGA).

Main Results:

• The SNCGA-based EMFLAP plan reduced planning time by 8.34% compared to SGA.
• SNCGA demonstrated a 20.25% reduction in calculation time versus SGA.
• SNCGA efficiently handled model constraints and solution descriptions, improving overall algorithm complexity and reducing time.

Conclusions:

• The proposed SNCGA is a superior algorithm for solving the EMFLAP model efficiently.
• SNCGA offers practical advantages in processing complex constraints and reducing computation time for disaster planning.
• This method provides valuable guidance for emergency management in designing effective EMFLAP decision schemes.