@misc{3028491,
  abstract     = {{We consider linear programming problems for which there is uncertainty about coefficient matrices in constraints and/or in goal function. The aim is to reduce such problems to (linear) optimization problems without uncertainty using imprecise decision theory. Our approach is based on reformulating of the original problem as a decision problem under uncertainty. We investigate what results can be obtained for different types of uncertainty models—linear and vacuous previsions, and possibility distributions—for two different optimality criteria: maximinity and maximality. We present some general results and simple illustrations.}},
  author       = {{Shariatmadar, Keivan and de Cooman, Gert}},
  keywords     = {{maximinity,imprecise decision making,linear programming,coherent lower prevision,hypograph.,maximality}},
  language     = {{eng}},
  pages        = {{23}},
  title        = {{Linear programming with uncertainty}},
  year         = {{2016}},
}