Location-allocation methodologies seek to locate one or
more health facilities or services in the most desirable place
with the use of heuristic or optimization techniques. Heuristic
methods often reflect more of the real world constraints
than optimization techniques by using a set of decision rules
algorithms) to find good solutions. Optimization techniques
produce the best solution to the locational query based on
stated constraints and objectives built into mathematical
models. Integer programming, tree-searching methods, and
linear programming are frequently used optimization techniques.
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Solutions to location-allocation problems have been
allied, perhaps excessively so, to methods and models that
require data and computer facilities that may be unavailable.
Heuristic and optimization techniques cannot be used when:
problems are too complex and require expensive or
excessive computer time; 2) techniques require inaccessible
expensive data; and 3) problems cannot be expressed as
linear equations because of the complexity of the relationship.2 Problems of this sort were sounded out in a recent
literature review that also called attention to the misguided
of certain methodologies in health and applied social
science research. It was argued that simpler and alternative
approaches to problem-solving should be considered.3
Problem
A real world problem required the allocation of an
emergency helicopter to one of three hospital service areas
sparsely populated rural northern Chile (Figure 1). A
review of location-allocation models suggested that they
were inappropriate in this case, according to points (1) and
stated above. Antecedents to the problem, as disclosed
local health officials,* were that population dispersion
could be used as a surrogate measure for emergency medical
need, and only population size and density need be
considered given the similar morbidity and socioeconomic