A Review on Applications of Evolutionary Algorithms to Reservoir Operation for Hydropower Production
Evolutionary Algorithms (EAs) have been used
widely through evolution theory to discover acceptable solutions that
corresponds to challenges such as natural resources management.
EAs are also used to solve varied problems in the real world. EAs
have been rapidly identified for its ease in handling multiple
objective problems. Reservoir operations is a vital and researchable
area which has been studied in the last few decades due to the limited
nature of water resources that is found mostly in the semi-arid
regions of the world. The state of some developing economy that
depends on electricity for overall development through hydropower
production, a renewable form of energy, is appalling due to water
scarcity. This paper presents a review of the applications of
evolutionary algorithms to reservoir operation for hydropower
production. This review includes the discussion on areas such as
genetic algorithm, differential evolution, and reservoir operation. It
also identified the research gaps discovered in these areas. The results
of this study will be an eye opener for researchers and decision
makers to think deeply of the adverse effect of water scarcity and
drought towards economic development of a nation. Hence, it
becomes imperative to identify evolutionary algorithms that can
address this issue which can hamper effective hydropower
generation.
[1] Wang, C., et al., Long-term scheduling of large cascade hydropower
stations in Jinsha River, China. Energy Conversion and Management,
2015. 90(0): p. 476-487.
[2] Bazmi, A.A. and G. Zahedi, Sustainable energy systems: Role of
optimization modeling techniques in power generation and supply—A
review. Renewable and Sustainable Energy Reviews, 2011. 15(8): p.
3480-3500.
[3] Lu, P., et al., Short-term hydro generation scheduling of Xiluodu and
Xiangjiaba cascade hydropower stations using improved binary-real
coded bee colony optimization algorithm. Energy Conversion and
Management, 2015. 91(0): p. 19-31.
[4] Xu, J. and Z. Tao, A class of multi-objective equilibrium chance
maximization model with twofold random phenomenon and its
application to hydropower station operation. Mathematics and
Computers in Simulation, 2012. 85(0): p. 11-33.
[5] Zhang, R., et al., Optimal operation of multi-reservoir system by multielite
guide particle swarm optimization. International Journal of
Electrical Power & Energy Systems, 2013. 48(0): p. 58-68.
[6] Kumar, N.K., S. Raju, and B. Ashok, Optimal reservoir operation for
irrigation of multiple crops using genetic algorithms. Journal of
Irrigation and Drainage Engineering, 2006. 132(2): p. 123-129.
[7] Yuan, X., et al., An enhanced differential evolution algorithm for daily
optimal hydro generation scheduling. Computers & Mathematics with
Applications, 2008. 55(11): p. 2458-2468.
[8] Storn, R. and K. Price, Differential evolution- A simple effeicient
adaptive scheme for global optimization over continuous spaces.
International computer science institute, ed. T.R.N. TR-95-012. 1995,
Calif: Berkley.
[9] Kennedy, J. and R.C. Eberhart. Particle swarm optimization. in
Proceedings of IEEE International Conference on Neural Networks.
1995.
[10] Malekmohammadi, B., R. Kerachian, and B. Zahraie, Developing
monthly operating rules for a cascade system of reservoirs: Applicationn
of Bayesian networks. Environmental Modelling & Software, 2009. 24:
p. 1420-1432.
[11] Naresh, R. and J. Sharma, Short term hydro scheduling using two-phase
neural network. International Journal of Electrical Power & Energy
Systems, 2002. 24(7): p. 583-590.
[12] Adeyemo, J.A., Reservoir operation using Multi-objective Evolutionary
Algorithms-A Review. Asian Journal of Scientific Research, 2011: p. 1-
12.
[13] Singh, A., Simulation–optimization modeling for conjunctive water use
management. Agricultural Water Management, 2014. 141(0): p. 23-29.
[14] Chung, T., Y. Li, and Z. Wang, Optimal generation expansion planning
via improved genetic algorithm approach. Interantional Journal of
Electrical Power, 2004. 26(8): p. 655-659.
[15] Madani, K., Game theory and water resources. Journal of Hydrology,
2010. 381(3-4): p. 225-238.
[16] Nicklow, J.W., et al., State of the art for genetic algorithm and beyond
in water resources planning and management. Journal of Water
Resource Planning and Management. ASCE, 2010. 136(4): p. 412-432.
[17] Reddy, M.J. and D.N. Kumar, Multiobjective Differential Evolution with
Application to Reservoir Optimization. Journal of Computer in Civil
Engineering, 2007. 21(2): p. 136-146.
[18] Azamathulla, H.M., et al., Comparison between genetic algorithm and
linear programming approach for real time operation. Journal of
Hydro-environment Research, 2008. 2: p. 172-181.
[19] Chang, L. and F. Chang, Multi-objective evolutionary algorithm for
operating parallel reservoir system. Journal of Hydrology, 2009. 377: p.
12-20. [20] Fogel, D.B., Evolutionary computation: principles and practice for
signal processing. 2000, Bellingham, Washington: SPIE press.
[21] Sarker, R. and T. Ray, An improved evolutionary algorithm for solving
multi-objective crop planning models. Computers and Electronics in
Agriculture, 2009. 68(2): p. 191-199.
[22] Blickle, T., Theory of evolutionary algorithms and applications to
system syntheis. 1997, Swiss Federal school of Technology: Zurich.
[23] Reddy, M.J. and D.N. Kumar, Optimal reservoir operation using multiobjective
evolutionary algorithm. Water Resources Management, 2006.
20: p. 861-878.
[24] Chang, L.C., Guiding rational reservoir flood operation using penaltytype
genetic algorithm. Journal of Hydrology, 2007. 354.
[25] Chang, L., et al., Constrained genetic algorithms for optimizing multiuse
reservoir operation. Journal of Hydrology, 2010. 390(1–2): p. 66-
74.
[26] Regulwar, D.G. and R.U. Kamodkar, Derivation of Multipurpose Single
Reservoir Release policies with Fuzzy Constraints. J. Water Resource
and Protection, 2010. 2: p. 1030-1041.
[27] Wang, K., L. Chang, and F. Chang, Multi-tier interactive genetic
algorithms for the optimization of long-term reservoir operation.
Advances in Water Resources, 2011. 34(10): p. 1343-1351.
[28] Rahimi, I., K. Qaderi, and A.M. Abasiyan, Optimal Reservoir Operation
Using MOPSO with Time Variant Inertia and Acceleration Coefficients.
Universal Journal of Agricultural Research, 2013. 1(3): p. 74-80.
[29] Afshar, M.H., Extension of the constrained particle swarm optimization
algorithm to optimal operation of multi-reservoirs system. Electrical
Power and Energy Systems, 2013. 51: p. 71-81.
[30] Wardlaw, R. and M. Sharif, Evaluation of genetic algorithm for optimal
reservoir system operstion. Journal of Water Resource Planning and
Management. , 1999. 125(1): p. 25-33.
[31] Bandyopadhyay, S. and S. Saha, Some Single- and Multiobjective
Optimization Techniques. 2013: Springer Berlin Heidelberg.
[32] Reddy, M.J. and D.N. Kumar, Computational algorithms inspired by
biological processes and evolution. Current Science, 2012. 103(4): p.
370-380.
[33] Cheng, C.T., W.C. Wang, and D.M. Xu, Optimizing Hydropower
Reservoir Operation Using Hybrid Genetic Algorithm and Chaos. Water
Resources Management, 2008. 22: p. 895-909.
[34] Srinivas, N. and D. Kalyanmoy, Multiobjective optimisation using
Nondominated Sorting in Genetic Algorithms. Journal of Evolutionary
Computation, 1994. 2(3): p. 221-248.
[35] Deb, K., et al., A fast and Elitist multiobjective Genetic Algorithm:
NSGA-II. IEEE Ttransactions of evolutionary computation, 2002. 6(2):
p. 182-197.
[36] Adeyemo, J.A., Application of Differential Evolution to water resources
management, in Department of civil engineering. 2009, Tshwane
university of Technology: Tshwane, Gauteng. p. 1-242.
[37] Rani, D. and M.M. Moreira, Simulation-Optimization modeling: a
survey and potential application in reservoir systems operation. . Water
Resources Management, 2010. 24: p. 1107-1138.
[38] Li, X., et al., A parallel dynamic programming algorithm for multireservoir
system optimization. Advances in Water Resources, 2014. 67:
p. 1-15.
[39] Zhang, Z., et al., An adaptive particle swarm optimiztion algorithm for
reservoir operation optimization. Applied Soft Computing, 2014. 18: p.
167-177.
[40] Chang, L., Guiding rational reservoir flood operation using penalty-type
genetic algorithm. Journal of Hydrology, 2008. 354(1–4): p. 65-74.
[41] Regulwar, D.G., S.A. Choudhari, and P.A. Raj, Differential evolution
algorithm with application to optimal operation of multipurpose
reservoir. Journal of Water Resource and Protection, 2010. 2: p. 560-
568.
[42] Karamouz, M., A. Ahmadi, and A. Moridi, Probabilistic reservoir
operation using bayesian stochastic model and support vector machine.
Advances in water resources, 2009. 32(11): p. 1588-1600.
[43] Zheng, F., A. Simpson, and A. Zecchin, Improving the efficiency of
multi-objective evolutionary algorithms through decomposition: An
application to water distribution network design. Environmental
Modelling & Software, 2014(0).
[44] Elferchichi, A., et al., The genetic algorithm approach for identifying the
optimal operation of a multi-reservoirs on-demand irrigation system.
Biosystems Engineering, 2009. 102(3): p. 334-344.
[45] Chen, L., J. Mcphee, and W.W.G. Yeh, A diversified multiobjective GA
for optimzing reservoir rule curves. Advances in Water Resources,
2007. 30: p. 1082-1093.
[46] Zhou, J., et al., Integrated optimization of hydroelectric energy in the
upper and middle Yangtze River. Renewable and Sustainable Energy
Reviews, 2015. 45(0): p. 481-512.
[47] Baños, R., et al., Optimization methods applied to renewable and
sustainable energy: A review. Renewable and Sustainable Energy
Reviews, 2011. 15(4): p. 1753-1766.
[48] Kıran, M.S., et al., A novel hybrid approach based on Particle Swarm
Optimization and Ant Colony Algorithm to forecast energy demand of
Turkey. Energy Conversion and Management, 2012. 53(1): p. 75-83.
[49] Li, C., et al., Improved decomposition–coordination and discrete
differential dynamic programming for optimization of large-scale
hydropower system. Energy Conversion and Management, 2014. 84(0):
p. 363-373.
[50] Yoo, J.H., Maximization of hydropower generation through the
application of a linear programming model. journal of Hydrology, 2009.
372((1-2)): p. 182-187.
[51] Kuby, M.J., et al., A multiobjective optimization model for dam removal:
an example of salmon passage with hydropower and water storage in
the Willamette basin. . Advances in Water Resources, 2005. 28(8): p.
845-855.
[52] Perez-Diaz, J.I., J.R. Wilhelmi, and J.A. Sanchez-Fernandez, Short term
operation scheduling of a hydropower plant in the day ahead electricity
market. Electrical Power Systems Research, 2010. 80(12): p. 1535-1542.
[53] Lee, T.Y., Short term hydroelectric power system scheduling with wind
turbine generators using multi-pass iteration particle swarm
optimization approach. Energy conversion and management, 2008.
49(4): p. 751-760.
[54] Li, A., et al., Application of immune algorithm-based particle swarm
optimization for optimized load distribution along cascade hydropower
station. Compter and mathematics with applications, 2009. 57: p. 1785-
1791.
[55] Doganis, P. and H. Sarimveis, Optimization of power production
through coordinated use of hydroelectric and conventional power units.
Applied Mathematical Modelling, 2014. 38(7–8): p. 2051-2062.
[56] Cai, W., et al., Optimized reservoir operation to balance human and
environmental requirements: A case study for the three Gorges and
Gezhouba Dams, Yangtze River basin, China. Ecological Informatics,
2013. 18: p. 40-48.
[57] Zhang, Z., et al., Use of parallel deterministic dynamic programming
and hierarchical adaptive genetic algorithm for reservoir operation
optimization. Computers & Industrial Engineering, 2013. 65: p. 310-321.
[58] Zhang, H., et al., An efficient multi-objective adaptive differential
evolution with chaotic neuron network and its application on long-term
hydropower operation with considering ecological environment
problem. International Journal of Electrical Power & Energy Systems,
2013. 45(1): p. 60-70.
[1] Wang, C., et al., Long-term scheduling of large cascade hydropower
stations in Jinsha River, China. Energy Conversion and Management,
2015. 90(0): p. 476-487.
[2] Bazmi, A.A. and G. Zahedi, Sustainable energy systems: Role of
optimization modeling techniques in power generation and supply—A
review. Renewable and Sustainable Energy Reviews, 2011. 15(8): p.
3480-3500.
[3] Lu, P., et al., Short-term hydro generation scheduling of Xiluodu and
Xiangjiaba cascade hydropower stations using improved binary-real
coded bee colony optimization algorithm. Energy Conversion and
Management, 2015. 91(0): p. 19-31.
[4] Xu, J. and Z. Tao, A class of multi-objective equilibrium chance
maximization model with twofold random phenomenon and its
application to hydropower station operation. Mathematics and
Computers in Simulation, 2012. 85(0): p. 11-33.
[5] Zhang, R., et al., Optimal operation of multi-reservoir system by multielite
guide particle swarm optimization. International Journal of
Electrical Power & Energy Systems, 2013. 48(0): p. 58-68.
[6] Kumar, N.K., S. Raju, and B. Ashok, Optimal reservoir operation for
irrigation of multiple crops using genetic algorithms. Journal of
Irrigation and Drainage Engineering, 2006. 132(2): p. 123-129.
[7] Yuan, X., et al., An enhanced differential evolution algorithm for daily
optimal hydro generation scheduling. Computers & Mathematics with
Applications, 2008. 55(11): p. 2458-2468.
[8] Storn, R. and K. Price, Differential evolution- A simple effeicient
adaptive scheme for global optimization over continuous spaces.
International computer science institute, ed. T.R.N. TR-95-012. 1995,
Calif: Berkley.
[9] Kennedy, J. and R.C. Eberhart. Particle swarm optimization. in
Proceedings of IEEE International Conference on Neural Networks.
1995.
[10] Malekmohammadi, B., R. Kerachian, and B. Zahraie, Developing
monthly operating rules for a cascade system of reservoirs: Applicationn
of Bayesian networks. Environmental Modelling & Software, 2009. 24:
p. 1420-1432.
[11] Naresh, R. and J. Sharma, Short term hydro scheduling using two-phase
neural network. International Journal of Electrical Power & Energy
Systems, 2002. 24(7): p. 583-590.
[12] Adeyemo, J.A., Reservoir operation using Multi-objective Evolutionary
Algorithms-A Review. Asian Journal of Scientific Research, 2011: p. 1-
12.
[13] Singh, A., Simulation–optimization modeling for conjunctive water use
management. Agricultural Water Management, 2014. 141(0): p. 23-29.
[14] Chung, T., Y. Li, and Z. Wang, Optimal generation expansion planning
via improved genetic algorithm approach. Interantional Journal of
Electrical Power, 2004. 26(8): p. 655-659.
[15] Madani, K., Game theory and water resources. Journal of Hydrology,
2010. 381(3-4): p. 225-238.
[16] Nicklow, J.W., et al., State of the art for genetic algorithm and beyond
in water resources planning and management. Journal of Water
Resource Planning and Management. ASCE, 2010. 136(4): p. 412-432.
[17] Reddy, M.J. and D.N. Kumar, Multiobjective Differential Evolution with
Application to Reservoir Optimization. Journal of Computer in Civil
Engineering, 2007. 21(2): p. 136-146.
[18] Azamathulla, H.M., et al., Comparison between genetic algorithm and
linear programming approach for real time operation. Journal of
Hydro-environment Research, 2008. 2: p. 172-181.
[19] Chang, L. and F. Chang, Multi-objective evolutionary algorithm for
operating parallel reservoir system. Journal of Hydrology, 2009. 377: p.
12-20. [20] Fogel, D.B., Evolutionary computation: principles and practice for
signal processing. 2000, Bellingham, Washington: SPIE press.
[21] Sarker, R. and T. Ray, An improved evolutionary algorithm for solving
multi-objective crop planning models. Computers and Electronics in
Agriculture, 2009. 68(2): p. 191-199.
[22] Blickle, T., Theory of evolutionary algorithms and applications to
system syntheis. 1997, Swiss Federal school of Technology: Zurich.
[23] Reddy, M.J. and D.N. Kumar, Optimal reservoir operation using multiobjective
evolutionary algorithm. Water Resources Management, 2006.
20: p. 861-878.
[24] Chang, L.C., Guiding rational reservoir flood operation using penaltytype
genetic algorithm. Journal of Hydrology, 2007. 354.
[25] Chang, L., et al., Constrained genetic algorithms for optimizing multiuse
reservoir operation. Journal of Hydrology, 2010. 390(1–2): p. 66-
74.
[26] Regulwar, D.G. and R.U. Kamodkar, Derivation of Multipurpose Single
Reservoir Release policies with Fuzzy Constraints. J. Water Resource
and Protection, 2010. 2: p. 1030-1041.
[27] Wang, K., L. Chang, and F. Chang, Multi-tier interactive genetic
algorithms for the optimization of long-term reservoir operation.
Advances in Water Resources, 2011. 34(10): p. 1343-1351.
[28] Rahimi, I., K. Qaderi, and A.M. Abasiyan, Optimal Reservoir Operation
Using MOPSO with Time Variant Inertia and Acceleration Coefficients.
Universal Journal of Agricultural Research, 2013. 1(3): p. 74-80.
[29] Afshar, M.H., Extension of the constrained particle swarm optimization
algorithm to optimal operation of multi-reservoirs system. Electrical
Power and Energy Systems, 2013. 51: p. 71-81.
[30] Wardlaw, R. and M. Sharif, Evaluation of genetic algorithm for optimal
reservoir system operstion. Journal of Water Resource Planning and
Management. , 1999. 125(1): p. 25-33.
[31] Bandyopadhyay, S. and S. Saha, Some Single- and Multiobjective
Optimization Techniques. 2013: Springer Berlin Heidelberg.
[32] Reddy, M.J. and D.N. Kumar, Computational algorithms inspired by
biological processes and evolution. Current Science, 2012. 103(4): p.
370-380.
[33] Cheng, C.T., W.C. Wang, and D.M. Xu, Optimizing Hydropower
Reservoir Operation Using Hybrid Genetic Algorithm and Chaos. Water
Resources Management, 2008. 22: p. 895-909.
[34] Srinivas, N. and D. Kalyanmoy, Multiobjective optimisation using
Nondominated Sorting in Genetic Algorithms. Journal of Evolutionary
Computation, 1994. 2(3): p. 221-248.
[35] Deb, K., et al., A fast and Elitist multiobjective Genetic Algorithm:
NSGA-II. IEEE Ttransactions of evolutionary computation, 2002. 6(2):
p. 182-197.
[36] Adeyemo, J.A., Application of Differential Evolution to water resources
management, in Department of civil engineering. 2009, Tshwane
university of Technology: Tshwane, Gauteng. p. 1-242.
[37] Rani, D. and M.M. Moreira, Simulation-Optimization modeling: a
survey and potential application in reservoir systems operation. . Water
Resources Management, 2010. 24: p. 1107-1138.
[38] Li, X., et al., A parallel dynamic programming algorithm for multireservoir
system optimization. Advances in Water Resources, 2014. 67:
p. 1-15.
[39] Zhang, Z., et al., An adaptive particle swarm optimiztion algorithm for
reservoir operation optimization. Applied Soft Computing, 2014. 18: p.
167-177.
[40] Chang, L., Guiding rational reservoir flood operation using penalty-type
genetic algorithm. Journal of Hydrology, 2008. 354(1–4): p. 65-74.
[41] Regulwar, D.G., S.A. Choudhari, and P.A. Raj, Differential evolution
algorithm with application to optimal operation of multipurpose
reservoir. Journal of Water Resource and Protection, 2010. 2: p. 560-
568.
[42] Karamouz, M., A. Ahmadi, and A. Moridi, Probabilistic reservoir
operation using bayesian stochastic model and support vector machine.
Advances in water resources, 2009. 32(11): p. 1588-1600.
[43] Zheng, F., A. Simpson, and A. Zecchin, Improving the efficiency of
multi-objective evolutionary algorithms through decomposition: An
application to water distribution network design. Environmental
Modelling & Software, 2014(0).
[44] Elferchichi, A., et al., The genetic algorithm approach for identifying the
optimal operation of a multi-reservoirs on-demand irrigation system.
Biosystems Engineering, 2009. 102(3): p. 334-344.
[45] Chen, L., J. Mcphee, and W.W.G. Yeh, A diversified multiobjective GA
for optimzing reservoir rule curves. Advances in Water Resources,
2007. 30: p. 1082-1093.
[46] Zhou, J., et al., Integrated optimization of hydroelectric energy in the
upper and middle Yangtze River. Renewable and Sustainable Energy
Reviews, 2015. 45(0): p. 481-512.
[47] Baños, R., et al., Optimization methods applied to renewable and
sustainable energy: A review. Renewable and Sustainable Energy
Reviews, 2011. 15(4): p. 1753-1766.
[48] Kıran, M.S., et al., A novel hybrid approach based on Particle Swarm
Optimization and Ant Colony Algorithm to forecast energy demand of
Turkey. Energy Conversion and Management, 2012. 53(1): p. 75-83.
[49] Li, C., et al., Improved decomposition–coordination and discrete
differential dynamic programming for optimization of large-scale
hydropower system. Energy Conversion and Management, 2014. 84(0):
p. 363-373.
[50] Yoo, J.H., Maximization of hydropower generation through the
application of a linear programming model. journal of Hydrology, 2009.
372((1-2)): p. 182-187.
[51] Kuby, M.J., et al., A multiobjective optimization model for dam removal:
an example of salmon passage with hydropower and water storage in
the Willamette basin. . Advances in Water Resources, 2005. 28(8): p.
845-855.
[52] Perez-Diaz, J.I., J.R. Wilhelmi, and J.A. Sanchez-Fernandez, Short term
operation scheduling of a hydropower plant in the day ahead electricity
market. Electrical Power Systems Research, 2010. 80(12): p. 1535-1542.
[53] Lee, T.Y., Short term hydroelectric power system scheduling with wind
turbine generators using multi-pass iteration particle swarm
optimization approach. Energy conversion and management, 2008.
49(4): p. 751-760.
[54] Li, A., et al., Application of immune algorithm-based particle swarm
optimization for optimized load distribution along cascade hydropower
station. Compter and mathematics with applications, 2009. 57: p. 1785-
1791.
[55] Doganis, P. and H. Sarimveis, Optimization of power production
through coordinated use of hydroelectric and conventional power units.
Applied Mathematical Modelling, 2014. 38(7–8): p. 2051-2062.
[56] Cai, W., et al., Optimized reservoir operation to balance human and
environmental requirements: A case study for the three Gorges and
Gezhouba Dams, Yangtze River basin, China. Ecological Informatics,
2013. 18: p. 40-48.
[57] Zhang, Z., et al., Use of parallel deterministic dynamic programming
and hierarchical adaptive genetic algorithm for reservoir operation
optimization. Computers & Industrial Engineering, 2013. 65: p. 310-321.
[58] Zhang, H., et al., An efficient multi-objective adaptive differential
evolution with chaotic neuron network and its application on long-term
hydropower operation with considering ecological environment
problem. International Journal of Electrical Power & Energy Systems,
2013. 45(1): p. 60-70.
@article{"International Journal of Earth, Energy and Environmental Sciences:71264", author = "Nkechi Neboh and Josiah Adeyemo and Abimbola Enitan and Oludayo Olugbara", title = "A Review on Applications of Evolutionary Algorithms to Reservoir Operation for Hydropower Production", abstract = "Evolutionary Algorithms (EAs) have been used
widely through evolution theory to discover acceptable solutions that
corresponds to challenges such as natural resources management.
EAs are also used to solve varied problems in the real world. EAs
have been rapidly identified for its ease in handling multiple
objective problems. Reservoir operations is a vital and researchable
area which has been studied in the last few decades due to the limited
nature of water resources that is found mostly in the semi-arid
regions of the world. The state of some developing economy that
depends on electricity for overall development through hydropower
production, a renewable form of energy, is appalling due to water
scarcity. This paper presents a review of the applications of
evolutionary algorithms to reservoir operation for hydropower
production. This review includes the discussion on areas such as
genetic algorithm, differential evolution, and reservoir operation. It
also identified the research gaps discovered in these areas. The results
of this study will be an eye opener for researchers and decision
makers to think deeply of the adverse effect of water scarcity and
drought towards economic development of a nation. Hence, it
becomes imperative to identify evolutionary algorithms that can
address this issue which can hamper effective hydropower
generation.", keywords = "Evolutionary algorithms, genetic algorithm,
hydropower, multi-objective, reservoir operations.", volume = "9", number = "9", pages = "1145-7", }
