  In sampling theory, stratification corresponds to a technique used in
surveys, which allows segmenting a population into homogeneous subpopulations
(strata) to produce statistics with a higher level of precision. In particular,
this article proposes a heuristic to solve the univariate stratification
problem - widely studied in the literature. One of its versions sets the number
of strata and the precision level and seeks to determine the limits that define
such strata to minimize the sample size allocated to the strata. A
heuristic-based on a stochastic optimization method and an exact optimization
method was developed to achieve this goal. The performance of this heuristic
was evaluated through computational experiments, considering its application in
various populations used in other works in the literature, based on 20
scenarios that combine different numbers of strata and levels of precision.
From the analysis of the obtained results, it is possible to verify that the
heuristic had a performance superior to four algorithms in the literature in
more than 94% of the cases, particularly concerning the known algorithms of
Kozak and Lavallee-Hidiroglou.
