Deregulation of the US telecommunication market and the break-up of the Bell System in the 1980s, followed by the privatizing and deregulating of most economically advanced countries of Europe and the Asia-Pacific region, ushered in an era of market turbulence, technological uncertainty and regulatory confusion in the information technology sector [Dholakia and Dholakia 1994]. This transformation from a regulated to a deregulated industry was the impetus for the marked drop in long-distance rates and the ensuing rapid upsurge in usage, particularly in data communication. In response to competition, common carriers have greatly increased the number of services available. For example, since divestiture, AT&T responded with hundreds of new interstate services and features; indeed, the number of new AT&T pricing plans and services rose from 35 in 1984 to 195 in 1991 [Garfinkel 1993]. Competition of this magnitude makes it possible for businesses to economically create vast private telecommunication networks. As society demands larger and more complex data communication networks, particularly to support such applications as end-user computing, multimedia, and electronic data interchange (EDI), designing cost effective networks and subsequently managing them becomes increasingly more difficult and consequential. One noteworthy topological issue in the design of data communication networks is how to connect large numbers of remote terminals to a central site. Historically, the usual design method utilized strictly concentrators [Mirzaian 1985]. However, with the advent of microelectronics, there is a host of data concentration equipment available that facilitate the economic utilization of transmission media. Despite many sophisticated alternatives supported by advanced technology, transmission will always constitute the most expensive component of telecommunication systems, requiring careful management to achieve the highest possible economy [Puzman and Kubin 1992]. This research addresses a network design problem referred to as the generalized concentrator equipment location problem (GCELP). GCELP may be defined as the problem of determining where to establish sites consisting of one or more concentrators, each of which are connected to a central site (e.g., a node of a backbone network or a processing site), and connecting terminals, often remote, to these concentrators. For completeness sake, variable concentrator coverage is considered. That is, each terminal will be connected to some concentrator for its primary coverage, possibly to another concentrator for its secondary coverage, and so forth [Pirkul et al.1988, Narasimhan 1990]. In the next section, a mathematical formulation of GCELP is provided