AGGREGATION BOUNDS IN STOCHASTIC LINEAR-PROGRAMMING

Stochastic linear programs for dynamic decision-making problems become extremely large and complex as additional uncertainties and possible future outcomes are included in their formulation. Row and column aggregation can significantly reduce this complexity, but the solutions of the aggregated problem only provide an approximation of the true solution. In this paper, error bounds on the value of the optimal solution of the original problem are obtained from the solution of the aggregated problem. These bounds apply for aggregation of both random variables and time periods.