A Decision Making Framework in Production Processes Using Bayesian Inference and Stochastic Dynamic Programming

2007 ◽  
Vol 7 (23) ◽  
pp. 3618-3627 ◽  
Author(s):  
Seyed Taghi Akhavan Ni ◽  
Mohammad Saber Fallah Nez
Keyword(s):  
Decision Making ◽  
Dynamic Programming ◽  
Bayesian Inference ◽  
Stochastic Dynamic Programming ◽  
Stochastic Dynamic ◽  
Production Processes ◽  
Decision Making Framework

An integrated neural network stochastic dynamic programming model for optimizing the operation policy of Aswan High Dam

2011 ◽  
Vol 42 (1) ◽  
pp. 50-67 ◽  
Author(s):  
A. H. El-Shafie ◽  
M. S. El-Manadely
Keyword(s):  
Neural Network ◽  
Decision Making ◽  
Dynamic Programming ◽  
Decision Maker ◽  
Stochastic Dynamic Programming ◽  
Programming Model ◽  
Stochastic Dynamic ◽  
Release Policy ◽  
Aswan High Dam ◽  
Decision Making Model

Developing optimal release policies of multipurpose reservoirs is very complex, especially for reservoirs within a stochastic environment. Existing techniques are limited in their ability to represent risks associated with deciding a release policy. The risk aspect of the decisions affects the design and operation of reservoirs. A decision-making model is presented that is capable of replicating the manner in which risks associated with reservoir release decisions are perceived, interpreted and compared by a decision-maker. The model is based on Neural Network (NN) theory. This decision-making model can be used with a Stochastic Dynamic Programming (SDP) approach to produce a NN-SDP model. The resulting integrated model allows the attitudes towards risk of a decision-maker to be considered explicitly in defining the optimal release policy. Clear differences in the policies generated from the basic SDP and the NN-SDP models are observed when examining the operation of Aswan High Dam (AHD). The NN-SDP model yields policies that are more reliable and resilient and less vulnerable than those obtained using the SDP model.

Sign in / Sign up

Export Citation Format

Share Document