Intuitionistic fuzzy optimization approach in optimal irrigation planning of Ukai-Kakrapar irrigation project, India

This article addresses the formulation of the Intuitionistic Multiobjective fuzzy linear programming (IFO MOFLP) model for optimal allocation of crops in the command area of the Ukai-Kakrapar Irrigation Project, India. Initially, the crisp linear programming (LP) model is applied to acquire the opti...

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Vydáno v:ISH journal of hydraulic engineering Ročník 29; číslo 3; s. 367 - 377
Hlavní autoři: Pawar, Sangita, Patel, Prem Lal, Mirajkar, A.B.
Médium: Journal Article
Jazyk:angličtina
Vydáno: 27.05.2023
Témata:
income
India
irrigation rates
uncertainty
India
ISSN:0971-5010, 2164-3040, 2164-3040
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Shrnutí:This article addresses the formulation of the Intuitionistic Multiobjective fuzzy linear programming (IFO MOFLP) model for optimal allocation of crops in the command area of the Ukai-Kakrapar Irrigation Project, India. Initially, the crisp linear programming (LP) model is applied to acquire the optimal results of three conflicting objectives, namely, maximization of net irrigation benefits (NIBs), minimization of cost of cultivation (CC) and maximization of revenue generation from industrial and municipal supplies (MI). Subsequently, the LP solutions were used to develop the IFO MOFLP, IFO MOFLP with a hesitation index and two-phase IFO MOFLP (TPIFO MOFLP) models for the complete command area. The performance of the aforesaid models is assessed in terms of irrigation intensity, degree of acceptance (α), rejection (β) and hesitation index (π) for inflows at 75% probability of exceedance. The irrigation intensity from the proposed TPIFO MOFLP model has been found to be 101.28%, while NIB, CC and MI from the proposed model are 10,575.38, 5438.42 and 2638.20 million Rs, respectively, with α = 0.64, β = 0.18 and π = 0.18, respectively. The proposed TPIFO MOFLP model has been compared better with MOFLP as the former gives additional uncertainty controlling parameters like α, β and π, which would help the planner to take better decisions for real-world problems.
Bibliografie:ObjectType-Article-1
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ISSN:0971-5010
2164-3040
2164-3040
DOI:10.1080/09715010.2022.2052988