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<p>Community healthcare has become a considerable portion of a country’s economy due to its significant impact. One of the most important fields in community healthcare is the delivery of emergency medical services (EMS), which are provided to patients outside the hospital before their transfer to the nearest medical center. There is an increasing need to deliver the most effective care to individuals using limited resources. In this context, optimization methods have been successfully applied to manage various problems in healthcare environments. However, there are a limited number of research papers that consider community EMS as an optimization problem. To address this gap, this paper considers the staff scheduling problem in the Community EMS (SSP-CEMS) field, which is modeled as the vehicle routing problem with time windows (VRP-TW). Therefore, a self-learning genetic algorithm (SLGA) is proposed based on reinforcement learning. This strategy is used to automatically update the GA parameters to detect the next promising search direction. Then, computational experiments are performed using Solomon’s benchmark instances. The obtained results demonstrate the competitive performance of the proposed SLGA relative to the classical GA across standard VRP-TW benchmarks, supporting its potential applicability to community EMS scheduling contexts.</p>

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Optimizing staff scheduling in community emergency medical services using a self-learning genetic algorithm

  • 期刊论文

  • 发表日期：

    2026年5月12日

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  • Ameni Azzouz,
  • Takwa Tlili,
  • Ines Hilali Jaghdam,
  • Feda Muhammed Abuhaimed

摘要

Community healthcare has become a considerable portion of a country’s economy due to its significant impact. One of the most important fields in community healthcare is the delivery of emergency medical services (EMS), which are provided to patients outside the hospital before their transfer to the nearest medical center. There is an increasing need to deliver the most effective care to individuals using limited resources. In this context, optimization methods have been successfully applied to manage various problems in healthcare environments. However, there are a limited number of research papers that consider community EMS as an optimization problem. To address this gap, this paper considers the staff scheduling problem in the Community EMS (SSP-CEMS) field, which is modeled as the vehicle routing problem with time windows (VRP-TW). Therefore, a self-learning genetic algorithm (SLGA) is proposed based on reinforcement learning. This strategy is used to automatically update the GA parameters to detect the next promising search direction. Then, computational experiments are performed using Solomon’s benchmark instances. The obtained results demonstrate the competitive performance of the proposed SLGA relative to the classical GA across standard VRP-TW benchmarks, supporting its potential applicability to community EMS scheduling contexts.