Scholarlink

<p>Among the most critical challenges in emergency situations is the allocation of patients to available healthcare resources, such as hospital beds. Efficient allocation strategies are necessary to minimize the burden on healthcare systems. This paper proposes a periodic bi-objective optimization model to address the allocation of patients to hospital beds during emergencies, such as epidemics or pandemics. The first objective is to minimize the transportation cost by assigning patients to the nearest hospitals, while the second objective focuses on minimizing the cost of assigning patients to available beds, ensuring full utilization of bed capacity. This model is formulated as a linear programming problem and solved using two different methods: weighted-sum approach and <Emphasis Type="BoldItalic">ϵ</Emphasis>-constraint. The effectiveness of the model is demonstrated using benchmark datasets, highlighting its ability to provide efficient and flexible allocation solutions. The results show strong performance in minimizing transportation and bed assignment costs.</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

A Periodic Bi-objective Patient Bed Assignment Problem in Pandemic Situations

  • 期刊论文

  • 发表日期：

    2026年3月20日

查看全文
  • Hela Jedidi,
  • Hajer Ben Romdhane,
  • Issam Nouaouri,
  • Saoussen Krichen

摘要

Among the most critical challenges in emergency situations is the allocation of patients to available healthcare resources, such as hospital beds. Efficient allocation strategies are necessary to minimize the burden on healthcare systems. This paper proposes a periodic bi-objective optimization model to address the allocation of patients to hospital beds during emergencies, such as epidemics or pandemics. The first objective is to minimize the transportation cost by assigning patients to the nearest hospitals, while the second objective focuses on minimizing the cost of assigning patients to available beds, ensuring full utilization of bed capacity. This model is formulated as a linear programming problem and solved using two different methods: weighted-sum approach and ϵ-constraint. The effectiveness of the model is demonstrated using benchmark datasets, highlighting its ability to provide efficient and flexible allocation solutions. The results show strong performance in minimizing transportation and bed assignment costs.