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Abstract
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Uncertainty theory has been initiated in 2007 by Liu, as an axiomatically developed
notion, which considers the uncertainty on data as a belief degree on the domain expert’s
opinion. Uncertain linear optimization is devised to model linear programs in an uncertain
environment. In this paper, we investigate the relation between uncertain linear
optimization and parametric programming. It is denoted that the problem can be converted
to parametric linear optimization problem, at which belief degrees play the role of
parameters, and parametric linear optimization with its rich literature provides insightful
interpretations. In a point of view, a strictly complementary optimal solution of problem
is known for the belief degree α = 0, as well as the associated optimal partition. One may
be interested in knowing the region of belief degrees (parameters) where this optimal
partition remains invariant for all parameter values (belief degrees) in this region. We
consider the linear optimization problem with uncertain rim data, i.e., the right-hand
side and the objective function data. The known results in the literature are translated
to the language of uncertainty theory, and
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