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A decomposition-based multiobjective evolutionary algorithm using Simulated Annealing for the ambulance dispatching

Meriem Hemici1, Djaafar Zouache2,3, Boualem Brahmi4

  • 1Department of Mathematics, University Mohamed El Bachir El Ibrahimi of Bordj Bou Arreridj, Bordj Bou Arreridj, Algeria.

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Summary
This summary is machine-generated.

This study introduces an improved algorithm (G-MOEA/D-SA) to optimize ambulance dispatching during COVID-19, enhancing emergency medical services (EMS) response times and efficiency.

Keywords:
Ambulance dispatching and relocation problemsCOVID-19 callsMultiobjective evolutionary algorithm based on decomposition (MOEA/D)Simulated annealing (SA)

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Area of Science:

  • Operations Research
  • Public Health
  • Computer Science

Background:

  • The COVID-19 pandemic significantly increased emergency calls, straining emergency medical services (EMS) globally.
  • Saudi Arabia faces unique challenges due to large pilgrim populations, exacerbating EMS strain during health crises.
  • Real-time ambulance dispatching and relocation problems (real-time ADRP) are critical issues during pandemics.

Purpose of the Study:

  • To address the real-time ambulance dispatching and relocation problem (real-time ADRP) exacerbated by the COVID-19 pandemic.
  • To propose an enhanced multi-objective evolutionary algorithm for optimizing ambulance routing and resource allocation.

Main Methods:

  • An improved Multi-Objective Evolutionary Algorithm based on Decomposition with Simulated Annealing (G-MOEA/D-SA) was developed.
  • The algorithm incorporates a convergence indicator based dominance relation (CDR) and an external archive using epsilon dominance to preserve optimal solutions.
  • Experiments were conducted using real-world data from Saudi Arabia during the COVID-19 pandemic.

Main Results:

  • The G-MOEA/D-SA algorithm demonstrated superior performance compared to existing algorithms like MOEA/D, MOEA/D-M2M, and NSGA-II.
  • Statistical analyses (ANOVA, Wilcoxon test) confirmed the outperformance of the proposed G-MOEA/D-SA algorithm.
  • The algorithm effectively optimizes ambulance routes for emergency COVID-19 calls, improving EMS efficiency.

Conclusions:

  • The G-MOEA/D-SA algorithm offers a robust solution for real-time ambulance dispatching and relocation problems.
  • This approach significantly enhances the efficiency and effectiveness of emergency medical services during health crises.
  • The study highlights the potential of advanced optimization algorithms in managing public health emergencies.