| Abstract |
This study considers stochastic discrete optimization problems in which feasible solutions remain feasible irrespective of the randomness of problem parameters. It uses the concept of risk associated with a solution to define optimal solutions to stochastic discrete optimization problems, and shows that a least risk solution can be obtained by solving a non-stochastic discrete optimization problem similar to the stochastic problem. |