Approximate dynamic programming based approach to process control and scheduling

Multi-stage decision problems under uncertainty are abundant in process industries. Markov decision process (MDP) is a general mathematical formulation of such problems. Whereas stochastic programming and dynamic programming are the standard methods to solve MDPs, their unwieldy computational requirements limit their usefulness in real applications. Approximate dynamic programming (ADP) combines simulation and function approximation to alleviate the 'curse-of-dimensionality' associated with the traditional dynamic programming approach. In this paper, we present the ADP as a viable way to solve MDPs for process control and scheduling problems. We bring forth some key issues for its successful application in these types of problems, including the choice of function approximator and the use of a penalty function to guard against over-extending the value function approximation in the value iteration. Application studies involving a number of well-known control and scheduling problems, including dual control, multiple controller scheduling, and resource constrained project scheduling problems, point to the promising potentials of ADP. (c) 2006 Elsevier Ltd. All rights reserved.
Publisher
PERGAMON-ELSEVIER SCIENCE LTD
Issue Date
2006-09
Language
ENG
Keywords

MODEL-PREDICTIVE CONTROL; IMPROVING HEURISTIC SOLUTIONS; STOCHASTIC LINEAR-PROGRAMS; TRAVELING SALESMAN PROBLEM; DUAL CONTROL; ALGORITHMIC FRAMEWORK; NONLINEAR PROCESSES; OPTIMIZATION; SYSTEMS; DECOMPOSITION

Citation

COMPUTERS CHEMICAL ENGINEERING, v.30, no.10-12, pp.1603 - 1618

ISSN
0098-1354
DOI
10.1016/j.compchemeng.2006.05.043
URI
http://hdl.handle.net/10203/87757
Appears in Collection
CBE-Journal Papers(저널논문)
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