Comparison of Policies Derived from Stochastic Dynamic Programming and Genetic Algorithm Models |
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Authors: | V Jothiprakash Ganesan Shanthi |
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Affiliation: | (1) Department of Civil Engineering, Indian Institute of Technology Bombay, Mumbai, 400 076, India;(2) Department of Civil Engineering, J.J. College of Engineering and Technology, Tiruchirappalli, 620 009, India |
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Abstract: | A comprehensive Genetic Algorithm (GA) model has been developed and applied to derive optimal operational strategies of a
multi-purpose reservoir, namely Perunchani Reservoir, in Kodaiyar Basin in Tamil Nadu, India. Most of the water resources
problem involves uncertainty, in order to see that the GA model takes care of uncertainty in the input variable, the result
of the GA model is compared with the performance of a detailed Stochastic Dynamic Programming (SDP) model. The SDP models
are well established and proved that it takes care of uncertainty in-terms of either implicit or explicit approach. In the
present study, the objective function of the models is set to minimize the annual sum of squared deviation from desired target
release and desired storage volume. In the SDP model the optimal policies are derived by varying the state variables from
3 to 9 representative class intervals, and then the cases are evaluated for their performance using a simulation model for
longer length of inflow data, generated using a Thomas–Fiering model. From the performance of the SDP model policies, it is
found that the system encountered irrigation deficit, whereas GA model satisfied the demand to a greater extent. The sensitivity
analysis of the GA model in selecting optimal population, optimal crossover probability and the optimal number of generations
showed the values of 150, 0.76 and 175 respectively. On comparing the performance of SDP model policy with GA model, it is
found that GA model has resulted in a lesser irrigation deficit. Thus based on the present case study, it may be concluded
that the GA model performs better than the SDP model. |
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Keywords: | Stochastic dynamic programming Genetic algorithm Multi-purpose reservoir operation Simulation Synthetic stream flow generation |
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