Genetic algorithms for the sequential irrigation scheduling problem
| dc.creator | Anwar, Arif A. | |
| dc.creator | Haq, Z. U. | |
| dc.date | 2013-07 | |
| dc.date | 2014-02-02T16:39:50Z | |
| dc.date | 2014-02-02T16:39:50Z | |
| dc.date.accessioned | 2026-06-27T18:42:36Z | |
| dc.description | A sequential irrigation scheduling problem is the problem of preparing a schedule to sequentially service a set of water users. This problem has an analogy with the classical single machine earliness/tardiness scheduling problem in operations research. In previously published work, integer program and heuristics were used to solve sequential irrigation scheduling problems; however, such scheduling problems belong to a class of combinatorial optimization problems known to be computationally demanding (NP-hard). This is widely reported in operations research. Hence, integer program can only be used to solve relatively small problems usually 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 (GA), simulated annealing, or tabu search methods need to be used. These need to be formulated carefully and tested thoroughly. The current research is to explore the potential of GA to solve the sequential irrigation scheduling problems. Four GA models are presented that model four different sequential irrigation scenarios. The GA models are tested extensively for a range of problem sizes, and the solution quality is compared against solutions from integer programs and heuristics. The GA is applied to the practical engineering problem of scheduling water scheduling to 94 water users. | |
| dc.identifier | https://hdl.handle.net/10568/34545 | |
| dc.identifier.uri | http://hdl.handle.net/123456789/163327 | |
| dc.language | en | |
| dc.publisher | Springer | |
| dc.rights | Limited Access | |
| dc.source | Anwar, Arif; Haq, Z. U. 2013. Genetic algorithms for the sequential irrigation scheduling problem. Irrigation Science, 31(4):815-829. doi: https://doi.org/10.1007/s00271-012-0364-y | |
| dc.subject | irrigation scheduling | |
| dc.subject | computer applications | |
| dc.subject | optimization methods | |
| dc.subject | artificial intelligence | |
| dc.subject | genetic processes | |
| dc.subject | algorithms | |
| dc.subject | water users | |
| dc.subject | models | |
| dc.subject | engineering | |
| dc.title | Genetic algorithms for the sequential irrigation scheduling problem | |
| dc.type | Journal Article |