Application of genetic algorithms for irrigation water scheduling

Description/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.