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This study tackles challenges in Emergency Medical Services (EMS) in Birmingham, UK, where rising emergency calls and traffic congestion hinder effective response. Using a multi-objective optimization model based on the minimum P-envy algorithm, the research minimizes response times, ensures equitable service distribution, and maintains cost-efficiency. The model incorporates seven objective functions addressing infrastructure costs, ambulance deployment, and population demand alignment, while adhering to constraints like budget caps and coverage feasibility. A Genetic Algorithm (GA) solves this NP-hard problem, optimizing EMS resource deployment. Results demonstrate significant improvements in EMS coverage and response efficiency, aiding Birmingham in meeting critical emergency targets.

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A Multi-objective Optimization Model for Equitable and Efficient Emergency Medical Service Allocation in Birmingham Using the Minimum P-envy Algorithm

  • 会议论文

  • 发表日期：

    2025年

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  • Nadia Zoubir,
  • Mohammed Reda Chbihi Louhdi,
  • Nihad Aghbalou,
  • Hicham Behja,
  • Yassine Zahidi,
  • Hanae Erroussou,
  • Mohamed El Moufid,
  • Hicham Medromi

摘要

This study tackles challenges in Emergency Medical Services (EMS) in Birmingham, UK, where rising emergency calls and traffic congestion hinder effective response. Using a multi-objective optimization model based on the minimum P-envy algorithm, the research minimizes response times, ensures equitable service distribution, and maintains cost-efficiency. The model incorporates seven objective functions addressing infrastructure costs, ambulance deployment, and population demand alignment, while adhering to constraints like budget caps and coverage feasibility. A Genetic Algorithm (GA) solves this NP-hard problem, optimizing EMS resource deployment. Results demonstrate significant improvements in EMS coverage and response efficiency, aiding Birmingham in meeting critical emergency targets.