We are attempting to find fast, accurate algorithms
for the facility location problem. Generally stated, the goal
is to select locations which are "near" to most of a given set
of data points. This problem has wide-ranging applications in
operations research (selecting locations for warehouses) and in
computer science (clustering of large databases, placement of
routers in a network).

The problem can be modeled using integer programming and solved
exactly for moderately sized instances. However for large instances
or situations where facility location is a subroutine in another
network design problem, the running time of integer programs can
be prohibitive. There has been a great deal of research into combinatorial
approaches to the problem (for example local search algorithms)
and approaches which work on very large databases using streaming
models. This previous work is theoretical in nature, proving worst-case
approximation guarantees of, at best, 1.52.

This project aims to measure the performance of combinatorial
approaches on realistic problem instances. We hope to show that
these techniques will perform better than their worst-case bounds
and provide viable alternatives to integer programming for time-sensitive
applications.

This material is based upon work supported by National Science
Foundation under Grant No. 0122581.
Any opinions, findings, and conclusions or recommendations expressed
in this material are those of the author(s) and do not necessarily
reflect the views of the National Science Foundation