A Multiobjective Mathematical Model for Optimal Irrigation Water Allocation

Sustainable irrigation and food security increasingly depend on efficient water resource management in the face of growing climatic and economic constraints. In this study, we develop two single objective optimization models for irrigation planning, one that maximizes net benefit and the other that minimizes environmental flow deficiency, and compare their performance with established models reported in previous studies. We then extend the analysis to a multiobjective programming formulation solved through scalarization and genetic approaches to evaluate trade-offs. Numerical experiments on the Muhuri Irrigation Project reveal three outcomes: (i) a complete scenario view with profits ranging from $ \$ 0.2 \times 10^9$ to $ \$ 1.497\times 10^9$ and environmental flow deficits between 0 and 1200 GL, where the 1200 GL represents the theoretical annual maximum under a 100 GL uniform monthly target; (ii) explicit trade-offs showing higher profits correspond to greater ecological shortfalls; and (iii) an integration based approach producing nearly 1000 Pareto optimal solutions within seconds, greatly outperforming earlier studies.