PUMA
Istituto di Studi sui Sistemi Intelligenti per l'Automazione     
Cervellera C., Chen V. C. P., Wen A. Optimization of a large-scale water reservoir network by stochastic dynamic programming with efficient state space discretization. In: European Journal of Operational Research, vol. 171 (3) pp. 1139 - 1151. Feature Cluster: Heuristic and Stochastic Methods in Optimization. Elsevier, 2006.
 
 
Abstract
(English)
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.
DOI: 10.1016/j.ejor.2005.01.022
Subject dynamic programming
large-scale optimization
applied probability
neural networks
natural resources


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