The introduction of uncertainty to mathematical programs greatly increases the size of the resulting optimization problems. Specialized methods that exploit program structures and advances in computer technology promise to overcome the computational complexity of certain classes of stochastic programs. In this paper we examine the progressive hedging algorithm for solving multi-scenario generalized networks. We present computational results demonstrating the effect of various internal tactics on the algorithm's performance. Comparisons with alternative solution methods are provided.
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Research supported in part by grants from the National Science Foundation (DCR-861-4057) and the Mathematical and Analytics Computation Center of IBM Corporation, New York.
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Mulvey, J.M., Vladimirou, H. Applying the progressive hedging algorithm to stochastic generalized networks. Ann Oper Res 31, 399–424 (1991). https://doi.org/10.1007/BF02204860
- Stochastic networks
- scenario analysis
- dynamic decision problems