Modified L-shaped Decomposition Method with Scenario Aggregation for a Two-Stage Stochastic Programming Problem

Ana Ušpurienė, Leonidas Sakalauskas, Gediminas Gricius


This paper introduces a method which is developed to solve two-stage stochastic programming problems in which first-stage region is unbounded and cannot be solved using traditional decomposition. We are using our proposed L-shaped decomposition method modification to solve such problems. In order to achieve a more accurate result, the number of scenarios generated in the optimization process must be large enough. If there is a large number of target variables, optimization takes a long time and uses a lot of resources. Thus, in order to reduce the number of iterations of the optimization process, the amount of resources used, and the calculating time needed to get the optimal solution, the aggregation approach is applied. This paper also presents results of our research on optimal parameters setting of the proposed method.



Stochastic programming, scenario analysis, aggregation, optimization under uncertainty, decomposition method, optimization using Cplex.

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Print ISSN: 1392-124X 
Online ISSN: 2335-884X