Linear optimization problems on permutations under probabilistic uncertainty: properties and solution

Abstract

Authors study properties of linear optimization problems under probabilistic uncertainty while defining a problem based on the linear order on the set of discrete random variables. Properties of unconditional problem are established whose coefficients of the goal function or multiset's elements (but not both simultaneously) are discrete random variables. Based on properties of the solution of an unconditional problem with deterministic coefficients, we prove solution's properties for the problem with the goal function's coefficients as discrete random variables. The scheme of the branch and bound method for solving the linear optimization problems on permutations under probabilistic uncertainty is proposed as well as rules of branching and truncation of sets.