Large-Scale Nonlinear Conjunctive Use Optimization Problem: Decomposition Algorithm
Publication: Journal of Water Resources Planning and Management
Volume 136, Issue 1
Abstract
A cyclic storage (CS) system is defined as physically-integrated and operationally interconnected surface water and groundwater subsystems with full direct interactions between the subsystems. The proposed definition treats surface and subsurface impoundment subsystems as competing and potentially interconnected parallel storage facilities that minimize most of the problems associated with large-scale surface impoundments for water supply purposes. This paper emphasizes on the development and implementation of a hybrid two-stage genetic algorithm (GA)-linear programming (LP) algorithm to optimize the design and operation of a nonlinear, nonconvex, and large-scale semidistributed, CS system in an irrigable area. Performance of the proposed model is tested with a 240-period problem where other approaches failed to locate a feasible solution. For optimal operation of the system, a set of operating rules are developed for the joint utilization of surface and subsurface storage capacities to meet a predefined demand minimizing construction and operation cost over a 20-seasonal planning period. Results show that CS dominates a noncyclic storage system both in cost and operation flexibility.
Get full access to this article
View all available purchase options and get full access to this article.
References
Abkhan Consulting Engineers. (2000). Report of studies for Kinevars dam, Abkhan Consulting Engineers, Tehran, Iran.
Afshar, A., Zahraei, A., and Mariño, M. A. (2008). “Cyclic storage design and operation optimization: Hybrid GA decomposition approach.” Int. J. of Civil Engineering, 6(1), 34–47.
Alba, E., Nebro, A., and Troya, J. (2002). “Heterogeneous computing and parallel genetic algorithms.” J. Parallel Distrib. Comput., 62, 1362–1385.
Alimohammadi, S. (2005). “Optimum design and operation of joint surface and ground water sytem-cyclic storage approach.” Ph.D. dissertation, Iran Univ. of Science and Technology, Tehran, Iran.
Alimohammadi, S., Afshar, A., and Ghaheri, A. (2005). “Unit response matrix coefficients Development: ANN approach.” Proc., WSEAS/IASME Int. Conf. on Systems Theory and Scientific Computation, World Scientific and Engineering Academy and Society, Greece, 17–25.
Alimohammadi, S., Afshar, A., and Mariño, M. A. (2009). “Cyclic storage system optimization: Semidistributed parameter approach.” J. Am. Water Works Assoc., 101(2), 90–103.
Barlow, P. M., Ahlfeld, D. P., and Dickerman, D. C. (2003). “Conjunctive-management models for sustained yield of stream-aquifer systems.” J. Water Resour. Plann. Manage., 129(1), 35–48.
Basagaoglu, H., Mariño, M. A., and Shumway, R. H. (1999). “ -Form approximating problem for a conjunctive water resource management model.” Adv. Water Resour., 23, 69–81.
Bredehoeft, J. D., and Young, R. A. (1983). “Conjunctive use of groundwater and surface water for irrigated agriculture risk version.” Water Resour. Res., 19(5), 1111–1121.
Cai, X., McKinney, D. C., and Lasdon, L. S. (2001). “Solving nonlinear water management models using a combined genetic algorithm and linear programming approach.” Adv. Water Resour., 24, 667–676.
Coe, J. J. (1990). “Conjunctive use-advantages, constraints, and examples.” J. Irrig. Drain. Eng., 116(3), 427–443.
Espinoza, F., and Minsker, B. S. (2006). “Development of the enhanced self-adaptive hybrid genetic algorithm (e-SAHGA).” Water Resour. Res., 42, W08501.
Gen, M., and Cheng, R. (2000). Genetic algorithms and engineering optimization, Wiley, New York.
Goldberg, D. E. (1989). Genetic algorithms in search, optimization and machine learning, Addison-Wesley, Reading, Mass.
Heidari, M., and Ranjithan, S. R. (1998). “A hybrid optimization approach to the estimation of distributed parameters in two dimensional confined aquifers.” J. Am. Water Resour. Assoc., 34(4), 909–920.
Holland, J. H. (1973). “Genetic algorithms and the optimal allocations of trials.” SIAM J. Comput., 2(2), 88–105.
Lettenmaier, D. P., and Burges, S. J. (1982). “Cyclic storage: A preliminary analysis.” Ground Water, 20(3), 278–282.
LINDO Systems Inc. (2004). LINGO user’s guide, LINDO Systems, Chicago.
McDonald, M. G., and Harbaugh, W. (1988). A modular three-dimensional finite difference groundwater flow model, U.S. Geological Survey, Reston, Va.
Reichard, E. G. (1995). “Groundwater surface water management with stochastic surface water supplies: Simulation-optimization approach.” Water Resour. Res., 31(11), 2845–2865.
Tang, Y., Reed, P., and Kollat, J. B. (2007). “Parallelization strategies for rapid and robust evolutionary multiobjective optimization in water resources applications.” Adv. Water Resour., 30(3), 335–353.
Thomas, H. E. (1978). “Cyclic storage, where are you now?” Ground Water, 16(1), 12–17.
Tu, M. Y., Tsai, F. T. C., and Yeh, W. W. G. (2005). “Optimization of water distribution and water quality by hybrid genetic algorithm.” J. Water Resour. Plann. Manage., 131(6), 431–440.
Wang, C., Montazeri, B., Liang, W. K., Sun, N. Z., and Yeh, W. W. G. (1995). “Model development for conjunctive use study of the San Jacinto basin, California.” Water Resour. Bull., 31(2), 227–241.
Information & Authors
Information
Published In
Copyright
© 2010 ASCE.
History
Received: Nov 27, 2006
Accepted: Apr 21, 2009
Published online: Dec 15, 2009
Published in print: Jan 2010
Authors
Metrics & Citations
Metrics
Citations
Download citation
If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.