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.
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.
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