Management of Multipurpose Multireservoir Using Fuzzy Interactive Method

Abstract

In this paper a fuzzy interactive method is proposed for efficient management of multipurpose multireservoir problems. The proposed method provides an option to decision maker (DM) to work in an interactive manner to achieve the conflicting objectives as close to their desired values as is practically feasible. In each iteration, fuzzy membership functions of various objectives are framed and combined into a single objective using the product operator. The single objective nonlinear optimization model thus framed in each iteration is numerically solved using genetic algorithm. The solution provides the values of the objectives which can be actually achieved keeping in view their aspired values as provided by DM. At the end of each iteration, DM has the option to modify the aspired values of one or more objectives keeping in view the results obtained by the algorithm thus far. The algorithm is stopped when DM feels satisfied with the results. The working of the proposed method has been demonstrated on the mathematical model of a realistic multipurpose multireservoir system taken from literature.

Multiobjective fuzzy optimization for sustainable groundwater management using particle swarm optimization and analytic element method

“…Deep et al . () developed fuzzy interactive method for efficient management of multipurpose multireservoir problems and applied to a case study. Two objectives, namely, irrigation and hydropower generation, were considered in fuzzy environment.…”

An adaptive artificial bee colony algorithm for long-term economic dispatch in cascaded hydropower systems

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Performance of the Equivalent Reservoir Modelling Technique for Multi-Reservoir Hydropower Systems

“…One can mention some sound papers: Deep et al (2009), Jothiprakash and Shanthi (2009), Panuwat et al (2009), Haddad et al (2008, Afshar and Moeini (2008), Li and Wei (2008), Cheng et al (2008). Those studies are concerned to optimization methods including fuzzy techniques, stochastic dynamic programming, genetic algorithms, neural networks, "honey-bee" mating optimization, "ant" algorithms and simulated annealing.…”