Aggregation for Large-Scale Location Problems

9522882弗朗西斯 本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用指标是客户从设施获得服务所需的距离。然而，当客户池较大时，可以聚合客户的来源以减小问题大小并提高计算效率。本研究的目的是采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这种聚合相关的质量或错误，因为聚合涉及使用近似的客户位置，而不是实际位置。显然，近似位置会导致客户实际到达设施的距离的近似解决方案。这项研究将开发和测试聚合方法，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。测试过程将涉及计算机执行的方法和测试使用真实世界的数据。 当有许多设施用户/客户时，聚合/近似的使用在应用设施定位中非常常见。尽管普遍接受的办法，很少有分析工作存在，以评估质量或汇总计划的错误。 本研究中所开发的聚合与测试演算法，其设计目的是为了保持近似误差较小。因此，该算法应该是有用的城市和区域规划师，运输/物流专家，地理学家谁必须选择聚合方案的大规模选址问题。

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.