Improved Dynamic Programming Methods for Optimal Control of Lumped-Parameter Stochastic Systems

C. Russell Philbrick () and Peter K. Kitanidis ()
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C. Russell Philbrick: Alstom Esca Corporation, 11120 NE 33rd Place, Bellevue, Washington 98004
Peter K. Kitanidis: Department of Civil and Environmental Engineering, Stanford University, M24 Terman Engineering Center, Stanford, California 94305-4020

Operations Research, 2001, vol. 49, issue 3, 398-412

Abstract: New dynamic programming methods are developed to solve stochastic control problems with a larger number of state variables than previously possible. These methods apply accurate interpolation to numerical approximation of continuous cost-to-go functions, greatly reducing the number of discrete states that must be evaluated. By efficiently incorporating information on first and second derivatives, the approximation reduces computational effort by several orders of magnitude over traditional methods. Consequently, it is practical to apply dynamic programming to complex stochastic problems with a larger number of state variables than traditionally possible. Results are presented for hypothetical reservoir control problems with up to seven state variables and two random inputs.

Keywords: Programming; stochastic: algorithms; Dynamic programming; Markov; infinite state: efficient approximation of cost-to-go functions; Natural resources; water resources: optimal reservoir operations (search for similar items in EconPapers)
Date: 2001
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