2014
DOI: 10.5120/15507-4276
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Intuitionistic Fuzzy Optimization Technique in Agricultural Production Planning: A Small Farm Holder Perspective

Abstract: Present paper is an application study of intuitionistic fuzzy optimization technique in agricultural production planning problem particularly a case of smallholder farmer in north Bihar, India. Generally, the crop planning problem is formulated as linear programming problems. but in realistic situation there are many uncertain factors in agricultural production planning problems and hence future profits for crop are imprecise and uncertain values. Therefore, we propose a model of crop planning using intuitioni… Show more

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Cited by 19 publications
(5 citation statements)
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“…A computational method for solving a multiobjective linear programming problem using an intuitive fuzzy optimization model is presented. To investigate how the model makes use of belonging/not-belonging status, a comparison of the effects of linear and nonlinear membership functions is provided [30]. A fuzzy multi-objective intuitionistic nonlinear programming model is developed for irrigation planning in both dry and wet conditions.…”
Section: Introductionmentioning
confidence: 99%
“…A computational method for solving a multiobjective linear programming problem using an intuitive fuzzy optimization model is presented. To investigate how the model makes use of belonging/not-belonging status, a comparison of the effects of linear and nonlinear membership functions is provided [30]. A fuzzy multi-objective intuitionistic nonlinear programming model is developed for irrigation planning in both dry and wet conditions.…”
Section: Introductionmentioning
confidence: 99%
“…Multiobjective fuzzy methodology having three goals was considered as maximization of net benefits, agricultural output, and labor employment for Pune city of Maharashtra State, India [27]. Another study was conducted by applying intuitionistic fuzzy optimization technique in agricultural production planning, with a focus on smallholder farmers in north Bihar, India [28].…”
Section: Introductionmentioning
confidence: 99%
“…Later, Atanassov and Gargov introduced interval-valued IFSs (IVIFSs), in which membership functions and non-membership functions are subintervals of a unit interval [0, 1] (Atanassov and Gargov, 1989). Similar to fuzzy sets, IFSs have wide applications in processing uncertain data for various purposes, such as decision-making, medical diagnoses, and agriculture (Szmidt and Kacprzyk, 1996;Xu, 2010;Papakostas et al, 2013;Shidpour et al, 2013;Bharati and Singh, 2014;Li and Zeng, 2015;Liu et al, 2016;Xuan Thao, 2018;Thao and Duong, 2019;Joshi, 2020;Garg and Kumar, 2020;Xue & Deng, 2020;. Along with distance and correlation measures, similarity measures of IFSs have been studied and widely used in many fields, such as decision-making, machine learning, and pattern recognition (Li and Cheng, 2002;Szmidt Kacprzyk, 2004;Xu, 2007;Ye, 2011;Hwang et al, 2012;Park et al, 2013;Rajarajeswari andUma, 2013 Shi andYe, 2013;Tian, 2013;Song et al, 2015;J.…”
Section: Introductionmentioning
confidence: 99%