Aggregation Methods for Large-Scale Location Problems

配送和城市服务领域的许多问题需要对设施位置的最佳选择做出决策，包括仓储、服务中心和零售决策。 计算机模型可以用来帮助这些决定。 这样的计算机模型通常需要关于客户（称为需求点）在哪里，有多少客户以及他们的需求是什么的信息。 在许多计算机模型应用中，需求点的数量可以达到数百万。 即使所有的需求点数据都是可用的，将其全部包含在模型中通常也是不可行的。 相反，请求点通常是聚合的;例如，可以假设一个邮政编码区域中的所有请求点都位于该邮政编码区域的中心。 这种聚合减小了模型的大小，但也产生了建模误差。 在实践中通常使用的聚合方案通常是ad-hoc的，很少或根本不使用问题结构，并且通常不关心它们的聚合错误。 该建议提出了进行需求点聚合的方法，以便将误差保持在可管理的限度内。 对于各种类别的位置问题，提出了建立在以前的工作的聚合方法的发展。 将对影响误差的模型参数进行研究，并对汇总方法进行计算机测试。 研究应有助于积累知识，最终将导致需求点聚合问题得到更好的理解和解决。 定位分析师将能够更好地平衡计算机模型所需解决方案的质量与聚合引入的误差。研究的结果应该有助于更好地决策facilitysiding时，这样的决定是基于计算机建模。 结果应该是有用的城市和区域规划师，运输/物流专家，地理学家，所有这些人有时都参与选择聚合方法的大规模选址问题。

Numerous problems in the area of distribution and in urban services require decisions about best choices of facility locations, including warehousing, service centers, and retailing decisions. Computer models can be used to help with these decisions. Such computer models usually require information on where the customers (referred to as demand points) are, how many of them there are, and what their demands are. In many computer model applications, the demand points can number in the millions. Even if all the demand point data is available, it is usually infeasible to include it all in the models. Instead, demand points are usually aggregated; for example, all the demand points in one postal code area may be assumed to be at the center of the postal code area. This aggregation reduces the size of the model, but also creates modeling error. Aggregation schemes typically used in practice are usually ad-hoc, make little or no use of the problem structure, and generally show no concern for them aggregation error. This proposal addresses means of doing demand point aggregation so as to keep the error to manageable limits. For various classes of location problems, the development of aggregation methods that build on previous work is proposed. Model parameters that affect the error will be studied, and computer testing of the aggregation methods will be performed. The research should contribute to a cumulative body of knowledge that will eventually result in demand point aggregation problems being better understood and solved. Location analysts will be better able to balance the quality of the solutions needed from computer models with the error introduced by the aggregation. The results of the research should be helpful in allowing better decisions about facilitysiting when such decisions are based on computer modeling. The results should be useful to urban and regional planners, transportation/logistics specialists, and geographers, all of whom at times are involved in choosing aggregation methods for large-scale location problems.