A Simplified Multi‐objective Genetic Algorithm Optimization Model for Canal Scheduling

A simplified Multi‐Objective Genetic Algorithm Optimization Model (MOM‐GA) for canal scheduling under unequal flow rates of distributary canals is presented in this paper. This MOM‐GA was designed for dynamic rotational scheduling with two objectives: to reduce fluctuations of flow rates of superior canals, and to reduce seepage losses of canal systems. This model was programmed in MATLAB using its genetic algorithm functions. Application of this model was demonstrated with a case study of the Nanguan Main Canal system (NMC) in the Gaoyou Irrigation Area, China. The results demonstrated that the MOM‐GA is an effective model for optimizing canal scheduling. NMC keeps running under a relatively steady range, and the seepage losses are reduced by around half that under current and binary optimized scheduling. The MOM‐GA is also sufficiently flexible to be applied to different levels in canal systems as a simplified approach for canal scheduling design and operation. The optimization results given by MOM‐GA can assist irrigators to make better canal scheduling decisions in each irrigation event.