Aggregation error bounds for a class of location models
成果类型：
Article
署名作者：
Francis, RL; Lowe, TJ; Tamir, A
署名单位：
State University System of Florida; University of Florida; University of Iowa; Tel Aviv University
刊物名称：
OPERATIONS RESEARCH
ISSN/ISSBN：
0030-364X
DOI：
10.1287/opre.48.2.294.12382
发表日期：
2000
页码：
294-307
关键词：
摘要：
Many location models involve distances and demand points in their objective function. In urban contexts, there can be millions of demand points. This leads to demand point aggregation, which produces error. We identify a general model structure that includes most such location models, and present a means of obtaining error bounds for all models with this structure. The error bounds suggest how to do the demand point aggregation so as to keep the error small.