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Constrained optimization problems under uncertainty with coherent lower previsions
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DOI：10.1016/j.fss.2012.02.004.png)
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Abstract
En 中文
We investigate a constrained optimization problem with uncertainty about constraint parameters. Our aim is to reformulate it as a (constrained) optimization problem without uncertainty. This is done by recasting the original problem as a decision problem under uncertainty. We give results for a number of different types of uncertainty models-linear and vacuous previsions, and possibility distributions-and for two common but different optimality criteria for such decision problems-maximinity and maximality. We compare our approach with other approaches that have appeared in the literature. (c) 2012 Elsevier B.V. All rights reserved.
Keywords:
Constrained optimization
Maximinity
Maximality
Coherent lower prevision
Linear prevision
Vacuous prevision
Possibility distribution
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