Multistage stochastic programming with stagewise-dependent objective coefficient uncertainty
Anthony Downward, Oscar Dowson, and Regan Baucke
Department of Engineering Science, University of Auckland
We present a new algorithm for solving linear multistage stochastic programming problems with objective function coefficients modelled as a stochastic process. This algorithm overcomes the difficulties of existing stochastic dual dynamic programming methods, which require discretisation of the objective coefficient states. Using an argument based on the finiteness of the set of possible cuts, we prove that the algorithm converges almost surely. Finally, we demonstrate the practical application of the algorithm on a hydro-bidding example with the spot-price modelled as an auto-regressive process.