Hesitant Intuitionistic Fuzzy Approach in Optimal Irrigation Planning in India

Abstract

The hesitant intuitionistic fuzzy optimization method optimizes multi-objective optimization problems under uncertainty and hesitation, and reflects the practical aspects of better decision-making. Hesitant intuitionistic fuzzy optimization (HIFO), a new optimization technique, has been suggested in the current study to find the best cropping pattern in the Kakrapar Right Bank Main Canal (KRBMC) command area of Ukai-Kakrapar Water Resources Project in India. The HIFO multi-objective fuzzy linear programming (HIFO MOFLP) result includes three objectives: maximization of net irrigation benefits (NIB), maximization of employment generation (EG), and minimization of cost of cultivation (CC), along with the appropriate constraints set. The performance of the aforesaid model is evaluated based on irrigation intensity, degree of acceptance (αr), and degree of rejection (βr) for inflows corresponding to 75% exceedance probability. The irrigation intensity from the study HIFO MOFLP model has been found to be 82.05%, while NIB, EG, and CC from the proposed model are 5572.31 million Rs, 14,287.27 thousand-man days, and 3429.99 million Rs, respectively. The proposed HIFO MOFLP model has been compared with the IFO MOFLP approach for the same command area and found to give improved results in the form of the irrigation intensity of the command area and objective function values. The current study demonstrates how hesitant fuzzy membership functions and non-membership functions can be applied to deal with uncertainty and hesitation in a real-world problem.