A hesitant fuzzy computational method for multi-objective fuzzy linear programming: application to the Gangapur reservoir system

This is the authors' abstract. We don't add key points for this paper.

This study develops and applies, for the first time, Hesitant Multi-Objective Fuzzy Linear Programming (HMOFLP) to optimize crop allocation in the Gangapur irrigation system, India. Solutions for HMOFLP model is obtained under the 75% dependable inflow condition, ensuring realistic and policy-relevant water availability scenarios. The model addresses four conflicting objectives: maximization of Net Irrigation Benefit (NIB), maximization of Employment Generation (EG), minimization of Cost of Cultivation (CC) and maximization of revenue generation from Domestic and Industrial supplies (DI). Comparative analysis with MOFLP (Multi-Objective Fuzzy Linear Programming) shows that HMOFLP achieves substantially higher irrigation intensity (130.62%), representing an increase of 20.98 % over the MOFLP solution (109.64 %). HMOFLP also provides improved objective function outcomes, delivering 523.95 million Rs higher NIB and 189.18 thousand Rs higher EG and 65.87 million Rs higher DI compared to MOFLP. This superior performance results from HMOFLP show the ability to incorporate fuzzy uncertainties and capture complex interactions among multiple objectives under variable hydrological conditions.