Paper Info
Title
A comparison of global and semi-local approximation in T-stage stochastic optimization
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.
Year
DOI
Venue
2011
10.1016/j.ejor.2010.08.002
European Journal of Operational Research
Keywords
Field
DocType
Markov processes,Dynamic programming,Neural networks,Semi-local approximation
Dynamic programming,Stochastic optimization,Mathematical optimization,Optimal control,Markov process,Demand forecasting,Artificial neural network,Stochastic programming,Mathematics,Kernel (statistics)
Journal
Volume
Issue
ISSN
208
2
0377-2217
Citations 
PageRank 
References 
14
0.92
11
Authors
2
Name
Order
Citations
PageRank
Cristiano Cervellera122623.63
Danilo Macciò26410.95