Irrigation Scheduling with Genetic Algorithms
Publication: Journal of Irrigation and Drainage Engineering
Volume 136, Issue 10
Abstract
A typical irrigation scheduling problem is one of preparing a schedule to service a group of outlets that may be serviced simultaneously. This problem has an analogy with the classical multimachine earliness/tardiness scheduling problem in operations research (OR). In previously published work, integer programming was used to solve irrigation scheduling problems; however, such scheduling problems belong to a class of combinatorial optimization problems known to be computationally demanding. This is widely reported in OR literature. Hence integer programs (IPs) can be used only to solve relatively small problems typically in a research environment where considerable computational resources and time can be allocated to solve a single schedule. For practical applications, metaheuristics such as genetic algorithms, simulated annealing, or tabu search methods need to be used. However, these need to be formulated carefully and tested thoroughly. The current research explores the potential of genetic algorithms to solve the simultaneous irrigation scheduling problem. For this purpose, two models are presented: the stream tube model and the time block model. These are formulated as genetic algorithms, which are then tested extensively, and the solution quality is compared with solutions from an IP. The suitability of these models for the simultaneous irrigation scheduling problem is reported.
Get full access to this article
View all available purchase options and get full access to this article.
References
Anwar, A. A., and Clarke, D. (2001). “Irrigation scheduling using mixed integer linear programming.” J. Irrig. Drain. Eng., 127(2), 63–69.
Anwar, A. A., and De Vries, T. T. (2004). “Irrigation scheduling II: Heuristics approach.” J. Irrig. Drain. Eng., 130(1), 17–25.
Beasley, D., Bull, D. R., and Martin, R. R. (1993). “An overview of genetic algorithms: Part 2, research topics.” University Computing, 15(4), 170–181.
Bishop, A. A., and Long, A. K. (1983). “Irrigation water delivery for equity between users.” J. Irrig. Drain. Eng., 109(4), 349–356.
Coley, D. A. (1999). An introduction to genetic algorithms for scientists and engineers, World Scientific Publishing Co. Pte. Ltd., Singapore.
Davis, L. (1991). A handbook of genetic algorithms, Van Nostrad Reinhold, New York.
De Vries, T. T. (2003). “Irrigation scheduling with integer programming.” Ph.D. thesis, School of Civil Engineering and the Environment, Univ. of Southampton, Southampton, U.K.
de Vries, T. T., and Anwar, A. A. (2004). “Irrigation scheduling I: Integer programming approach.” J. Irrig. Drain. Eng., 130(1), 9–16.
de Vries, T. T., and Anwar, A. A. (2006). “Irrigation scheduling with travel times.” J. Irrig. Drain. Eng., 132(3), 220–227.
Goldberg, D. E. (1989). Genetic algorithms in search, optimization and machine learning, Addison-Wesley Publishing Co., Inc., Redwood City, Calif.
Haq, Z. U., Anwar, A. A., and Clarke, D. (2008). “Evaluation of a genetic algorithm for the irrigation scheduling problem.” J. Irrig. Drain. Eng., 134(6), 737–744.
Heady, R. B., and Zhu, Z. (1998). “Minimizing the sum of job earliness and tardiness in a multimachine system.” Int. J. Prod. Res., 36, 1619–1632.
Medaglia, A. L., and Gutiérrez, E. (2006a). “JGA: An object-oriented framework for rapid development of genetic algorithms.” Handbook of research on nature inspired computing for economics and management, J. P. Rennard, ed., IGI Global, Pennsylvania.
Medaglia, A. L., and Gutiérrez, E. (2006b). “Applications of JGA to operations management and vehicle routing.” Handbook of research on nature inspired computing for economics and management, J. P. Rennard, ed., IGI Global, Pennsylvania.
Michalewicz, Z. (1992). Genetic programs, 3rd Ed., Springer, New York.
Reddy, J. M., Wilamowski, B., and Sharmasarkar, F. C. (1999). “Optimal scheduling of irrigation for lateral canals.” ICID J., 48(3), 1–12.
Suryavanshi, A. R., and Reddy, J. M. (1986). “Optimal operation schedule of irrigation distribution systems.” Agric. Water Manage., 11, 23–30.
Wang, Z., Reddy, J. M., and Feyen, J. (1995). “Improved 0-1 programming model for optimal flow scheduling in irrigation canals.” Irrig. Drain. Syst., 9, 105–116.
Wardlaw, R., and Bhaktikul, K. (2004). “Comparison of genetic algorithm and linear programming approaches for lateral canal scheduling.” J. Irrig. Drain. Eng., 130(4), 311–317.
Information & Authors
Information
Published In
Copyright
© 2010 ASCE.
History
Received: Aug 24, 2009
Accepted: Feb 23, 2010
Published online: Feb 25, 2010
Published in print: Oct 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.