An Effective Cost Lower Bound for Multistage Stochastic Linear Programming
نویسندگان
چکیده
Multistage Stochastic Linear Programming (SLP) suffers from scenario explosion as the number of stages or the number of stochastic parameters increases. In this case, either one solves the SLP model approximately or one solves an approximation to the SLP model. In this situation it is useful to have some cost bound in order to assess the quality of approximated solutions. In this paper we introduce an effective cost lower bound which is computationally tractable and tighter than the lower bound given by the expected value problem. This new bound is obtained by constraint and decision aggregation in the SLP model with randomness appearing exclusively on the right-hand-side. We present some preliminary computational results.
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