Title of article :
A comparison of global and semi-local approximation in T-stage stochastic optimization
Author/Authors :
C. Cervellera، نويسنده , , D. Macci?، نويسنده ,
Issue Information :
روزنامه با شماره پیاپی سال 2011
Pages :
10
From page :
109
To page :
118
Abstract :
The paper presents a comparison between two different flavors of nonlinear models to be used for the approximate solution of T-stage stochastic optimization (TSO) problems, a typical paradigm of Markovian decision processes. Specifically, the well-known class of neural networks is compared with a semi-local approach based on kernel functions, characterized by less demanding computational requirements. To this purpose, two alternative methods for the numerical solution of TSO are considered, one corresponding to the classic approximate dynamic programming (ADP) and the other based on a direct optimization of the optimal control functions, introduced here for the first time. Advantages and drawbacks in the TSO context of the two classes of approximators are analyzed, in terms of computational burden and approximation capabilities. Then, their performances are evaluated through simulations in two important high-dimensional TSO test cases, namely inventory forecasting and water reservoirs management.
Keywords :
Markov processes , Neural networks , Dynamic programming , Semi-local approximation
Journal title :
European Journal of Operational Research
Serial Year :
2011
Journal title :
European Journal of Operational Research
Record number :
1313043
Link To Document :
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