Solving multistage stochastic networks: An application of scenario aggregation

John M. Mulvey · Hercules Vladimirou

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Risk and Portfolio Optimization Complexity and Algorithms in Graphs Advanced Optimization Algorithms Research

Abstract

Abstract The scenario aggregation algorithm is specialized for stochastic networks. The algorithm determines a solution that does not depend on hindsight and accounts for the uncertain environment depicted by a number of appropriately weighted scenarios. The solution procedure decomposes the stochastic program to its constituent scenario subproblems, thus preserving the network structure. Computational results are reported demonstrating the algorithm's convergence behavior. Acceleration schemes are discussed along with termination criteria. The algorithm's potential for execution on parallel multiprocessors is discussed.

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2019Theory of games and economic behavior 1946Theory of Games and Economic Behavior 1991Scenarios and Policy Aggregation in Optimization Under Uncertainty 1988Numerical Techniques for Stochastic Optimization 1985Decomposition and Partitioning Methods for Multistage Stochastic Linear Programs 1982The value of the stochastic solution in stochastic linear programs with fixed recourse 1986A Bank Asset and Liability Management Model 1992Stochastic Network Programming for Financial Planning Problems

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1993A Massively Parallel Algorithm for Nonlinear Stochastic Network Problems 1995Multistage stochastic programs: The state-of-the-art and selected bibliography 1993Application of the scenario aggregation approach to a two-stage, stochastic, common component, inventory problem with a budget constraint

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