Application of genetic algorithms for irrigation water scheduling

Abstract

A typical irrigation scheduling problem is one of preparing a schedule to service a group of outlets. These outlets may either be serviced sequentially or simultaneously. This problem has an analogy with the classical earliness/tardiness machine scheduling problems in operations research (OR). In previous published work integer programme were used to solve such problems; however, such scheduling problems belong to a class of combinatorial problems known to be computationally demanding (NP-hard). This is widely reported in OR. Hence integer programme 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 meta-heuristics such as genetic algorithms, simulated annealing or tabu search methods need to be used. However as reported in the literature, these need to be formulated carefully and tested thoroughly.
This thesis demonstrates how arranged-demand irrigation scheduling problems can be correctly formulated and solved using genetic algorithms (GA). By interpreting arrangeddemand irrigation scheduling problems as single or multi-machine scheduling problems, the wealth of information accumulated over decades in OR is capitalized on. The objective is to schedule irrigation supplies as close as possible to the requested supply time of the farmers to provide a better level of service. This is in line with the concept of Service Oriented Management (SOM), described as the central goal of irrigation modernization in recent literature. This thesis also emphasizes the importance of rigorous evaluation of heuristics such as GA.
First, a series of single machine models is presented that models the warabandi (rotation) type of irrigation distribution systems, where farmers are supplied water sequentially. Next, the multimachine models are presented which model the irrigation water distribution systems where several farmers may be supplied water simultaneously. Two types of multimachine models are defined. The simple multimachine models where all the farmers are supplied with identical discharges and the complex multimachine models where the farmers are allowed to demand different discharges. Two different approaches i.e. the stream tube approach and the time block approach are used to develop the multimachine models. These approaches are evaluated and compared to determine the suitability of either for the irrigation scheduling problems, which is one of the significant contributions of this thesis. The multimachine models are further enhanced by incorporating travel times which is an important part of the surface irrigation canal system and need to be taken into account when determining irrigation schedules. The models presented in this thesis are unique in many aspects. The potential of GA for a wide range of irrigation scheduling problems under arranged demand irrigation system is fully explored through a series of computational experiments.

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ORCID for Arif Anwar: orcid.org/0000-0002-3071-3197

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