James Scott (University of Cambridge)

Stochastic programme decomposition techniques for finance and logistics
Wednesday 20 March 2002 at 15.30, JCMB 6310

Abstract

We present a fast, robust, regularized nested Benders' decomposition code which has been used to solve stochastic programming problems which arise from practical financial planning and logistics applications. The code features a stochastic program presolver, as well as aggregation procedures. For certain classes of problem, we have found that an appropriate regularization term in the objective function can speed convergence, and alleviate the numerical instability associated with standard decomposition methods. We also demonstrate how Benders' decomposition can be used to solve problems with a convex nonlinear objective function and how it can be specialized to handle problems with a non-Markovian constraint structure. Solution times are given which compare favourably to those of currently available deterministic and stochastic solvers.

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