Irrigation Scheduling with Genetic Algorithms

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.