Aggregation in Dynamic Programming
James C. Bean, John R. Birge, Robert L. Smith
Operations Research · 1987 · Volume 35 · Issue 2
Abstract
Reducing the size of a dynamic program through state aggregation can significantly reduce both the data and the computation time required to solve a problem. We develop a new algorithm that combines state aggregation and disaggregation steps within a single-pass procedure. The solution obtained is automatically feasible for the original problem. By exploiting general conditions on the aggregate structure, we develop bounds for assessing the error from optimality introduced by the aggregation, and we illustrate with an application in infinite horizon optimization.
Keywords
114 state aggregation, 621 aggregation in dynamic programming
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