Scholarlink

<p>Efficient deployment of emergency medical services (EMS) is critical for maximizing survival in life threatening situations while ensuring adequate coverage for non-critical calls. This study proposes a novel stochastic, survival probability and time dependent location allocation model for two ambulance types (advanced life support ALS and basic life support BLS) and two demand categories (critical and non-critical). A priority-based optimization framework ensures critical demand are served first, while improving non-critical coverage. To efficiently solve the NP-hard problem, a Memetic algorithm enhanced with iterated simulated annealing (MA-ISA) is developed, enabling optimal allocation of ambulances and demand assignments. Computational experiments in a real urban district demonstrate improved survival rates for critical calls and enhanced coverage for non-critical calls, offering a practical, dynamic tool for EMS resource planning under uncertainty.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Maximizing weighted survival for coverage location model

  • 期刊论文

  • 发表日期：

    2026年6月14日

查看全文
  • Huda Zaki Naji

摘要

Efficient deployment of emergency medical services (EMS) is critical for maximizing survival in life threatening situations while ensuring adequate coverage for non-critical calls. This study proposes a novel stochastic, survival probability and time dependent location allocation model for two ambulance types (advanced life support ALS and basic life support BLS) and two demand categories (critical and non-critical). A priority-based optimization framework ensures critical demand are served first, while improving non-critical coverage. To efficiently solve the NP-hard problem, a Memetic algorithm enhanced with iterated simulated annealing (MA-ISA) is developed, enabling optimal allocation of ambulances and demand assignments. Computational experiments in a real urban district demonstrate improved survival rates for critical calls and enhanced coverage for non-critical calls, offering a practical, dynamic tool for EMS resource planning under uncertainty.