Improving the performance of Stochastic Dual Dynamic Programming

This paper is concerned with tuning the Stochastic Dual Dynamic Programming algorithm to make it more computationally efficient. We report the results of some computational experiments on a largescale hydrothermal scheduling model developed for Brazil. We find that the best improvements in computation time are obtained from an implementation that increases the number of scenarios in the forward pass with each iteration and selects cuts to be included in the stage problems in each iteration. This gives an order of magnitude decrease in computation time with little change in solution quality.