Geoscience Reference
In-Depth Information
Applications of FLPs arise in an wide variety of contexts. The topic by Drezner
and Hamacher (
2002
) surveys different applications of fixed-charge facility location
in such diverse areas as the public sector, software for GIS or robotics. Furthermore,
fixed-charge facility location also plays a critical role in many other areas like supply
chain management, distributed systems, humanitarian relief, emergency systems,
location-routing problems or freight transportation. Melo et al. (
2009
)survey
facility location models in the context of supply chain management until 2009.
Klose and Drexl (
2005
) summarize applications of FLPs within distributed system
design. The paper by Balcik and Beamon (
2008
) is a recent sign of the interest of
the combination of both humanitarian relief analysis and facility location models.
Further examples of applications can be found in Owen and Daskin (
1998
), Daskin
et al. (
2002
), Nagy and Salhi (
2007
) and Jiaa et al. (
2007
). In fact, the applicability
of fixed-charge facility location models goes beyond the area of Location Analysis.
Some fixed-charge facility location models are also valid within other fields like
machine scheduling, cluster analysis or combinatorial auctions (Escudero et al.
2009
;KloseandDrexl
2005
; Singh
2008
).
It has been traditionally assumed that in FLPs location decisions are strate-
gic, whereas allocation decisions are tactical or operational. There are potential
applications, however, in which location and allocation decisions are at the same
hierarchy level in the decision making process. One example of application in which
both decisions are strategic can be found in the design of a backbone network
in telecommunications. An example of application in which both decisions are
operational can be faced by some logistic companies which, at each time period,
have to solve a FLP to determine the warehouses locations and the distribution
pattern to be applied within the corresponding period.
Because FLPs are difficult optimization problems with many potential applica-
tions the study of their properties and efficient solution methods is of interest on
its own. A further motivation for this study is that it sets the basis for the analysis
of more complex models related to FLP extensions. In some cases, these extensions
can, in turn, be modeled as some basic FLP. For example, some multi-period facility
location problems (see Chap.
11
) or some hub-arc location problems (see Chap.
12
)
can be can be reduced to the FLPs studied here (see, for instance Albareda-Sambola
et al.
2009a
; Contreras and Fernández
2013
).
There are indeed a number of issues that define the characteristics of FLPs.
These will be discussed in this chapter and include the possibility of satisfying
the demand of each of the users from more than one facility, or capacity limits on
the maximum demand that can be served from any selected facility, among others.
Furthermore, several alternative formulations can be valid for a given FLP. Usually,
none of these alternatives has a clear advantage over the others although, as it often
happens with other discrete optimization problems, each of them is better suited
for a certain solution technique. We aim to give the reader a broad overview of
the main elements that may intervene in the solution process of FLPs, namely,
modeling assumptions and their implications, characteristics of formulations and
their relation to other formulations, properties of the domains, and appropriate
solution techniques. However, in order to keep the length of the chapter within
