A parallel computation approach for solving multistage stochastic network problems

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This paper presents a parallel computation approach for the efficient solution of very large multistage linear and nonIinear network problems with random parameters. These problems resul t from particular instances of models for the robust optimization of network problems with uncertainty in the values of the right-hand side and the objective function coefficients. The methodology considered here models the uncertainty using scenarios to characterize the random parameters. A. scenario tree is generated and, through the use of full-recourse techniques, an implementable solution is obtained for each group of scenarios at each stage along the planning horizon. As a consequence of the size of the resulting problems, and the special structure of their constraints, these models are particularly well-suited for the application of decomposition techniques, and the solution of the corresponding subproblems in a parallel computation environment. An Augmented Lagrangian decomposition algorithm has been implemented on a distributed computation environment, and a static load balancing approach has been chosen for the parallelization scheme. given the subproblem structure of the model. Large problems -9000 scenarios and 14 stages with a deterministic equivalent nonlinear model having 166000 constraints and 230000 variables- are solved in 15 minutes on a cluster of 4 small (16 Mflops) workstations. An extensive set of computational experiments is reported; the numerical results and running times obtained for our test set, composed of large-scale real-life problems, confirm the efficiency of this procedure.
Large-scale optimization, Augmented lagrangian decomposition, Distributed computation
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