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http://dx.doi.org/10.3741/JKWRA.2005.38.9.737

A Study on Objective Functions for the Multi-purpose Dam Operation Plan in Korea  

Eum, Hyung-Il (School of Civil, Urban & Geosystem Engrg., Seoul National Univ.)
Kim, Young-Oh (School of Civil, Urban & Geosystem Engrg., Seoul National Univ.)
Yun, Ji-Hyun (Department of Water Supply Operation & Maintenance, Korea Water Resources Corporation)
Ko, Ick-Hwan (Hydrosystems Engineering Center, Korea Institute of Water & Environment)
Publication Information
Journal of Korea Water Resources Association / v.38, no.9, 2005 , pp. 737-746 More about this Journal
Abstract
Optimization is a process that searches an optimal solution to obtain maximum or minimum value of an objective function. Many researchers have focused on effective search algorithms for the optimum but few researches were interested in establishing the objective function. This study compares two approaches for the objective function: one allows a tradeoff among the objectives and the other does not allow a tradeoff by assigning weights for the absolute priority between the objectives. An optimization model using sampling stochastic dynamic programming was applied to these two objective functions and the resulting optimal policies were compared. As a result, the objective function with no tradeoff provides a decision making process that matches practical reservoir operations than that with a tradeoff allowed. Therefore, it is more reasonable to establish the objective function with no a tradeoff among the objectives for multi-purpose dam operation plan in Korea.
Keywords
Objective function; Priority; Sampling stochastic dynamic programming; Dam operation plan;
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  • Reference
1 Keeney, R.L. (1992). Value-focused Thinking: A Path to Creative Decisionmaking. Harvard University Press, Cambridge
2 Hasebe, M., and Nagayarna, Y. (2002). 'Reservoir operation using the neural network and fuzzy systems for dam control and operation support.' Advances in Engineering Software, ELSEVIER, Vol. 33, pp. 245-260   DOI   ScienceOn
3 Hobbs, B.F., and Hepenstal A. (1989). 'Is optimization optimistically biased?' Water Resources Research, AGU, Vol. 25, No. 2, pp. 152-160   DOI
4 Goicoechea, A., Hansen, D.R., and Duckstein, L. (1982). Multiobjective Decision Analysis with Engineering and Business Applications. John Wiley & Sons, New York., pp. 19-23
5 Haimes, Y.Y., and Hall W.A. (1974). 'Multiobjectives in water resources systems analysis: the surrogate worth trade-off method.' Water Resources Research, AGU, Vol. 10, No. 4, pp. 615-624   DOI
6 Ginn, T.R., and Houck, M.H. (1989). 'Calibration of an objective function for the optimization of real-time reservoir operations.' Water Resources Research, AGU, Vol. 25, No. 4, pp. 591-603   DOI
7 Ekelund, R.B., and Tollison, R.D. (1988). Economics, Scott, Foresman and Company, Illinois, pp. 521-522
8 Barros, M., Tsai, F., Yang, S.L., Lopes, J., and Yeh, W. (2003). 'Optimization of large-scale hydropower system operations.' Journal of Water Resources Planning and Management, ASCE, Vol. 129, No. 3, pp. 178-188   DOI   ScienceOn
9 Cohon, J.L., and Marks, D.H. (1973). 'Multiobjective screening models and water resources investment.' Water Resources Research, AGU, Vol. 9, No. 4, pp. 826-836   DOI
10 한국수자원공사 (2004). 실시간물관리운영시스템구축기술개발, 과학기술부, 연구보고서
11 Zadeh, L. (1963). 'Optimality and non-scalar-valued performance criteria.' IEEE Tansactions on Automatic Control, IEEE, Vol. 8, No. 1, pp. 59-60   DOI
12 Tejada-Guibert, J., Stedinger, J.R., and Staschus, K. (1990). 'Optimization of the value of CVP's hydropower production.' Journal of Water Resources Planning and Management, ASCE, Vol. 116, No. 1, pp. 52-70   DOI
13 Trezos, T. (1991). 'Integer programming application for planning of hydropower production.' Journal of Water Resources Planning and Management, ASCE, Vol. 117, No. 3, pp. 340-351   DOI
14 Sharma, V., Jha, R., and Naresh R. (2004). 'Optimal multi-reservoir network control by two-phase neural network.' Electric Power Systems Research, ELSEVIER, Vol. 68, pp. 221-228   DOI   ScienceOn
15 Stedinger, J.R., Sule, B.F., and Loucks, D.P. (1984). 'Stochastic dynamic programming models for reservoir operation optimization.' Water Resources Research, AGU, Vol. 20, No. 11, pp. 1499-1505   DOI
16 Needham, J., Watkins, D., Lund, J., and Nanda, K. (2000). 'Linear programming for flood control in the Iowa and Des Moines rivers.' Journal of Water Resources Planning and Management, ASCE, Vol. 126, No. 3, pp. 118-127   DOI   ScienceOn
17 Rao, S.S. (1996). Engineering optimization, John Wiley & Sons, New York
18 Marglin, S.A. (1967). Public Investment Criteria. MIT Press, Cambridge
19 Martin, Q. (1983). 'Optimal operation of multiple reservoir systems.' Journal of Water Resources Planning and Management, ASCE, Vol. 109, No. 1, pp. 58-74   DOI
20 Nash, S., and Safer, A. (1996). Linear and nonlinear programming, McGraw-Hill, New York
21 Huang, W.C., Yuan, L.C., and Lee, C.M. (2002). 'Linking genetic algorithm with stochastic dynamic programming to the long-term operation of a multireservoir system.' Water Resources Research, AGU, Vol. 38, No. 12, pp. 40-1-40-9   DOI   ScienceOn
22 Kelman, J., Stedinger, J.R., Cooper, L.A., Hsu, E., and Yuan S.Q. (1990). 'Sampling stochastic dynamic programming applied to reservoir operation.' Water Resources Research, AGU, Vol. 26, No. 2, pp. 447-454   DOI
23 Kim, Y-O., and Palmer, R.N. (1997). 'Value of seasonal flow forecasts in Bayesian stochastic programming.' Journal of Water Resources, Planning and Management, ASCE, Vol. 123, No. 6, pp. 327-335   DOI
24 Keeney, R.L., and Wood, E.F. (1977). 'An illustrative example of the use of multiattribute utility theory for water resource planning.' Water Resources Research, AGU, Vol. 13, No. 4, pp. 705-712   DOI