在中国,基于贝叶斯概率理论和K-means集群分析的冷链物流园的位置优化
Lu Wang1, Xuannuo Liu2, Xuhui Wang2
1College of Landscape Architecture and Arts, Northwest A&F University, Yangling, 712100, China. garfield13yp@nwafu.edu.cn.
Scientific reports
|November 14, 2025
概括
本研究介绍了贝叶斯网络和K-means集群模型,用于最佳的冷链物流园区选址. 江苏省苏州被确定为最合适的地点.
科学领域:
- 物流和供应链管理的物流和供应链管理.
- 地理信息系统 (GIS) 是指地理信息系统.
- 运营研究 运营研究
背景情况:
- 为冷链物流园区有效地选址对于规划和建设至关重要.
- 传统方法缺乏对结果可靠性的定量标准.
- 需要一个科学和可持续的模型来确定最佳的地点.
研究的目的:
- 为冷链物流园区建立一个科学和可持续的选址模型.
- 介绍贝叶斯概率理论和K-means集群用于选址.
- 在江苏省使用GIS技术验证该模型.
主要方法:
- 构建一个包含影响因素的贝叶斯网络模型.
- 应用K-means集群分析用于选址.
- 使用贝叶斯歧视分析验证聚类可靠性.
- 使用GIS技术开发一个全面的适用性评估系统.
主要成果:
- 贝叶斯网络和K-means集群模型有效量化了选址因素.
- 苏州被确定为江苏省最适合冷链物流园区的城市.
- 经过详细的适用性评估,在苏州确定了最佳区域.
结论:
- 开发的模型为冷链物流园区选址提供了科学和可靠的方法.
- 贝叶斯理论,集群和GIS的整合提高了评估的准确性.
- 这些发现支持冷链基础设施开发的知情决策.
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