Optimization of a large-scale water reservoir network by stochastic dynamic programming with efficient state space discretization

To link to full-text access for this article, visit this link: http://dx.doi.org/10.1016/j.ejor.2005.01.022 Byline: Cristiano Cervellera (a), Victoria C.P. Chen (b), Aihong Wen (b) Keywords: Dynamic programming; Large-scale optimization; Applied probability; Neural networks; Natural resources Abstract: A numerical solution to a 30-dimensional water reservoir network optimization problem, based on stochastic dynamic programming, is presented. In such problems the amount of water to be released from each reservoir is chosen to minimize a nonlinear cost (or maximize benefit) function while satisfying proper constraints. Experimental results show how dimensionality issues, given by the large number of basins and realistic modeling of the stochastic inflows, can be mitigated by employing neural approximators for the value functions, and efficient discretizations of the state space, such as orthogonal arrays, Latin hypercube designs and low-discrepancy sequences. Author Affiliation: (a) Institute of Intelligent Systems for Automation, ISSIA-CNR National Research Council of Italy, Genova Branch, Via De Marini 6, Genova 16149, Italy (b) Department of Industrial and Manufacturing Systems Engineering, The University of Texas at Arlington, Campus Box 19017, Arlington, TX 76019-0017, United States