Aggregation for Large-Scale Location Problems

9522882弗朗西斯这项研究关注的是为大量客户提供服务的设施的位置。衡量选址决策质量的一个常见指标是客户从设施获得服务所需的路程。然而，当客户池较大时，可以聚合客户来源以减少问题规模并提高计算效率。这项研究的目的是使用聚合方法来开发和解决设施选址问题，并使用该模型来校准与此类聚合相关的质量或误差，因为聚合涉及使用近似客户位置而不是实际位置。显然，大致的位置可以得出客户实际到达工厂的距离的近似解决方案。这项研究将开发和测试聚集方法，这些方法旨在控制错误并利用特殊的问题结构来提高计算效率。测试过程将包括方法的计算机实施和使用真实世界的数据进行测试。当设施用户/客户较多时，在应用设施选址中，聚集/近似的使用非常普遍。尽管这种方法得到了普遍的接受，但几乎没有分析工作来评估聚合方案的质量或错误。本研究中开发的聚合和测试算法旨在保持较小的逼近误差。因此，该算法对城市和区域规划者、运输/物流专家和地理学家来说应该是有用的，他们必须为大规模选址问题选择聚集方案。

9522882 Francis This research is concerned with the location of a facility to serve a large number of customers. A common measure of the quality of a location decision is the distances that customers will have to travel to receive service from the facility. However, when the customer pool is large, the sources of customers may be aggregated to reduce problem size and improve computational efficiency. The objective of this research is to employ aggregation methods to develop and solve facility location problems and to use the model to calibrate the quality or error associated with such aggregation since aggregation involves using approximate customer locations instead of actual locations. Approximate locations obviously lead to an approximate solution for the distances customers will actually travel to reach the facility. The research will develop and test aggregation methods which are designed to control error and exploit special problem structures to improve computational efficiency. The testing process will involve both computer implementation of the methods and testing using real-world data. The use of aggregation/approximation is very common in applied facility location when there are many facility users/customers. Despite the common acceptance of the approach, little analytical work exists to evaluate the quality or error of aggregation schemes. The developed aggregation and testing algorithms in this research are designed to keep approximation error small. Thus, the algorithm should be useful to urban and regional planners, transportation/logistics specialists, and geographers who must choose aggregation schemes for large -scale location problems.