<?xml version="1.0" encoding="utf-8" standalone="yes"?>
<item>
  <id>05621876</id>
  <dt>a</dt>
  <an>05621876</an>
  <augroup>
    <au>He, Xiaozheng</au>
    <au>Chen, Anthony</au>
    <au>Chaovalitwongse, Wanpracha Art</au>
    <au>Liu, Henry</au>
  </augroup>
  <ti>On the quadratic programming approach for hub location problems.</ti>
  <so>Chaovalitwongse, Wanpracha (ed.) et al., Optimization and logistics challenges in the enterprise. New York, NY: Springer (ISBN 978-0-387-88616-9/hbk; 978-0-387-88617-6/ebook). Springer Optimization and Its Applications 30, 211-228 (2009).</so>
  <py>2009</py>
  <pu>New York, NY: Springer</pu>
  <lagroup>
    <la>EN</la>
  </lagroup>
  <ccgroup>
  </ccgroup>
  <utgroup>
  </utgroup>
  <cigroup>
  </cigroup>
  <ligroup>
    <li>doi:10.1007/978-0-387-88617-6_7</li>
  </ligroup>
  <abgroup>
    <ab>Summary: Hub networks play an important role in many real-life network systems such as transportation and telecommunication networks. Hub location problem is concerned with identifying appropriate hub locations in a network and connecting an efficient hub-and-spoke network that minimizes the flow-weighted costs across the network. This chapter is focused on the uncapacitated single allocation $p$-hub median problem (USA$p$HMP), which arises in many real-world hub networks of logistics operations. There have been many approaches used to solve this problem. We herein focus on a quadratic programming approach, which has been proven very effective and efficient. This approach incorporates the use of the linearization for 0-1 quadratic program. In this chapter, we give a brief review of the linearization techniques for 0-1 quadratic programs and compare the performance of several existing linearization techniques for USA$p$HMP. Toward the end, we discuss some properties, comments and possible developments of these linearization techniques in the real-life USA$p$HMP.</ab>
    <rv></rv>
  </abgroup>
</item>