Optimization of Hospital Inpatient Department Layout Based on Integrated SLP-QAP Model
- VernacularTitle:基于SLP-QAP融合模型的医院住院部布局优化研究
- Author:
Yuling LUO
1
;
Jie PAN
1
Author Information
1. 四川大学华西公共卫生学院·华西第四医院 四川 成都 610041;四川大学健康城市发展研究中心·西部农村卫生发展研究中心 四川 成都 610041
- Publication Type:Journal Article
- Keywords:
hospital;
layout optimization;
Systematic Layout Planning;
Quadratic Assignment Problem
- From:
Chinese Hospital Management
2025;(9):74-76
- CountryChina
- Language:Chinese
-
Abstract:
Objective To address operational challenges in hospital inpatient department layouts,such as resource misallocation,it proposes an integrated Systematic Layout Planningand(SLP)-Quadratic Assignment Problem(QAP)optimization method.By quantifying functional correlations among departments and patient flow density,this approach aims to reconfigure floor layouts to enhance operational efficiency and management effectiveness.Methods Based on three-year disease data,one-year departmental flow data,and inter-floor distance measurements from a tertiary hospital in Deyang City,a dual-dimensional"function-process"correlation matrix was constructed.The QAP model was then applied to transform multi-objective optimization into a mathematical programming problem,and a Hybrid Encoding Genetic Algorithmwas utilized to generate floor allocation schemes prioritizing clinical collaboration and resource-intensive allocation.Results The optimized layout reduced total operational costs(flow×distance)by 27.6%,achieved adjacent placement for high-frequency collaborative departments,and allocated high-traffic departments to floors adjacent to core functional areas.Conclusion From an operational optimization perspective,this study establishes a data-driven decision-making tool for hospital layout reconfiguration.The integrated SLP-QAP model enhances inter-departmental collaboration and resource allocation efficiency,aligning with the requirements of the Healthy China 2030 Planning Outline.Future research should incorporate dynamic cost forecasting and regional disease spectrum adaptation mechanisms to improve the long-term management value of the model.
