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作 者:滕辰妹 项寅 李善良[3] TENG Chenmei;XIANG Yin;LI Shanliang(School of Urban Governance and Public Affairs,Suzhou City University,Suzhou 215104,China;School of Business,Suzhou University Science and Technology,Suzhou 215006,China;School of Politics and Public Administration,Soochow University,Suzhou 215006,China)
机构地区:[1]苏州城市学院城市治理与公共事务学院,苏州215104 [2]苏州科技大学商学院,苏州215006 [3]苏州大学政治与公共管理学院,苏州215006
出 处:《系统工程理论与实践》2024年第12期4084-4096,共13页Systems Engineering-Theory & Practice
基 金:教育部人文社会科学青年项目(21YJC630126);江苏省高校哲学社会科学项目(2021SJA1364)。
摘 要:面临人口老龄化和慢性疾病高发的双重压力,合理配置卫生资源以适应不断变化的卫生需求至关重要.本研究提出了一种创新性的跨部门协同配置模型,旨在改进不同阶段卫生需求的动态响应.研究通过分析卫生需求阶段性变化与资源分配的关联,揭示了卫生服务优化的关键点.为了有效求解此模型,研究设计并改进了遗传算法,引入判别算子和新型编码策略,以提升算法性能和解的适用性.算例和灵敏度分析验证了模型具有更高的响应效率,尤其是在预算充足时,模型展现了在低成本下满足多样化卫生需求的潜力.通过P值统计分析,与现有技术相比,所提算法在解决问题时表现出更高的精确度和效率,展示了对未来卫生资源管理的实际应用价值.Facing the twin challenges of an aging population and a high incidence of chronic diseases,the rational distribution of healthcare resources to adapt to the dynamically changing healthcare demands is critically important.This study introduces an innovative cross-sector collaborative allocation model aimed at enhancing the dynamic response to healthcare needs at various stages.Through analyzing the phased evolution of healthcare demand and its correlation with resource distribution,the research uncovers pivotal points for optimizing healthcare services.To solve this model effectively,the study has developed and refined a hierarchical genetic algorithm,introducing discriminant operators and a novel encoding strategy to boost algorithm performance and the suitability of solutions.Case studies and sensitivity analysis have verified the model’s heightened efficiency in response,particularly when the budget is ample,revealing the model’s capacity to fulfill diverse healthcare needs cost-effectively. P-value statistical analysisindicates that, in comparison to existing methods, our proposed algorithm demonstrates superiorprecision and efficiency in tackling practical problems, showing its real-world application valuein future healthcare resource management.
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