@techreport{TD:101540,
	att_abstract={{This paper describes a biased random-key genetic algorithm for a
  real-world wireless backhaul network design problem.  This is a novel
  problem, closely related to variants of the Steiner tree problem and
  the facility location problem.  %We seek to build one or more $h$-hop
  trees Given a parameter $h$, we want to build a forest where each tree
  has at most $h$ hops. Each tree is rooted at specific nodes, called
  root nodes, and has leaves at demand nodes, where traffic originates.
  Candidate Steiner nodes do not have any demand but represent locations
  where we can install cellsites to cover the traffic and equipment to
  backhaul the traffic to the cellular core network.  Each Steiner node
  can cover demand nodes within a given distance, subject to a capacity
  constraint.  The aggregate set of constraints may make it impossible to
  cover or backhaul all demands.  A revenue function computes the revenue
  associated with the total amount of traffic covered and backhauled
  to the root nodes.  The objective of the problem is to build a forest
  that maximizes the difference between the total revenue and the cost
  associated with the installed equipment.  Although we will have a
  forest when we consider only the backhaul links and root nodes, the
  addition of demand vertices can induce undirected cycles, resulting
  in a directed acyclic graph.  We consider instances of this problem with 
  several additional constraints that are motivated by the requirements of 
  real-world telecommunication networks.}},
	att_authors={cd338h, wz1750, rs5419, kr1518, rd2518},
	att_categories={C_BB.5, C_CCF.2, C_CCF.7, C_CCF.8},
	att_copyright={{Elsevier}},
	att_copyright_notice={{The definitive version was published in 2015. {{, 2015-03-31}}
}},
	att_donotupload={},
	att_private={false},
	att_projects={},
	att_tags={Wireless backhaul network design, small cells, genetic algorithm, mixed integer programming model},
	att_techdoc={true},
	att_techdoc_key={TD:101540},
	att_url={http://web1-clone.research.att.com:81/techdocs_downloads/TD:101540_DS1_2014-12-18T21:46:00.535Z.pdf},
	author={Carlos De andrade and Mauricio Resende and Weiyi Zhang and Rakesh Sinha and Kenneth Reichmann and Robert Doverspike and F. K. Miyazawa},
	institution={{Applied Soft Computing}},
	month={March},
	title={{A biased random-key genetic algorithm for wireless backhaul network design}},
	year=2015,
}