On optimistic, pessimistic and mixed approaches under different membership functions for fully intuitionistic fuzzy multiobjective nonlinear programming problems

Mahajan, Sumati; Gupta, S. K.

doi:10.1016/j.eswa.2020.114309

Cited by 20 publications

(4 citation statements)

References 41 publications

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“…Wan and Li [36] applied the IF programming method to solve the selection problem of green suppliers under a set of constraints. Mahajan and Gupta [21] proposed IF optimistic, pessimistic, and mixed approaches for finding compromise solutions and applied them to transportation and production problems. Gupta et al [14] and Raghav et al [24] suggested compromise sample allocation procedures using the IF programming approach for estimating multivariate population means in stratified random sampling under a deterministic cost function.…”

Section: Solution Methodologymentioning

confidence: 99%

Compromise optimum allocation in neutrosophic multi-character survey under stratified random sampling using neutrosophic fuzzy programming

Ullah,

Shabbir,

Alomair

et al. 2024

Heliyon

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Section: Solution Methodologymentioning

confidence: 99%

Compromise optimum allocation in neutrosophic multi-character survey under stratified random sampling using neutrosophic fuzzy programming

Ullah,

Shabbir,

Alomair

et al. 2024

Heliyon

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“…Shivani et al [30] studied multiobjective NLP with rough interval parameters which originates for the management of solid wastes, the model aimed to optimize the cost of waste transportation, cost of waste treatment and the revenue generated from various treatment facilities. Sumati Maharajan et al [31] developed a simplified novel goal programming method under intuitionistic fuzzy environment using both membership/non-membership functions. Additionlly, it helps to obtain a Pareto optimal solution to a multiobjective quadratic programming problem.…”

Section: Literature Reviewmentioning

confidence: 99%

An optimization model for fuzzy nonlinear programming with Beale's conditions using trapezoidal membership functions

Kaliyaperumal,

2024

Proyecciones (Antofagasta)

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Non-linear Programming (NLP) is an optimization technique for determining the optimum solution to a broad range of research issues. Many times, the objective function is non-linear, owing to various economic behaviors such as demand, cost, and many others. Since the appearance of Kuhn and Tucker's fundamental theoretical work, a general NLP problem can be resolved using many methods to find the optimum solution. In this chapter, a fuzzy mathematical model based on Beale's condition is proposed to address NLP with inequality constraints in terms of fuzziness. Furthermore, the model demonstrates how quadratic programming problems can be solved using membership functions. The model also describes three stages: that is, mathematical formulation, computational procedures, and numerical illustration with comparative analysis. Likewise, the model illustrates the considered problem using two distinct approaches, namely membership functions (MF) and robust ranking index. Finally, the comparison analysis provides detailed results and discussion that justify the optimal outcome in order to address the vagueness of certain NLPPs.

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“…Angelov [ 1 ] has investigated an optimization technique that was based on the intersection property of intuitionistic fuzzy sets, called an intuitionistic fuzzy optimization technique, and it finds a better optimal solution of an optimization problem after fuzzy optimization technique. Recently, Mahajan [ 23 , 24 ] and Senthil et al [ 16 – 19 ] have studied IFS and applied it in several real-life optimization problems. An interval-valued intuitionistic fuzzy set (IVIFS) is a further extension of IFS and is investigated by Atanassov and Gargov [ 4 ] in which the membership and nonmembership degrees of an element are, respectively, represented by intervals in [0, 1] rather than fixed real numbers between 0 and 1.…”

Section: Literature Reviewmentioning

confidence: 99%

An Interval-Valued Intuitionistic Hesitant Fuzzy Methodology and Application

Bharati

2021

New Gener. Comput.

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Using advantages of interval-valued intuitionistic hesitant fuzzy sets (IVIHFS) for describing the hesitant and intuitionistic decisions of experts and identifying the limitations of previous research works about optimization techniques, this paper introduces a new optimization technique and provides a new computational algorithm, applicable in various real life multiobjective optimization problem (MOOP) of engineering and management sectors, and for this, a new operation between IVIHFSs is first introduced. On the basis of this concept, a stepwise computational algorithm is constructed, and it is an extension of both fuzzy and intuitionistic fuzzy optimization techniques. Finally, the proposed algorithm is illustrated using a production planning problem, and the obtained results are compared with the existing optimization techniques.

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On optimistic, pessimistic and mixed approaches under different membership functions for fully intuitionistic fuzzy multiobjective nonlinear programming problems

Cited by 20 publications

References 41 publications

Compromise optimum allocation in neutrosophic multi-character survey under stratified random sampling using neutrosophic fuzzy programming

Compromise optimum allocation in neutrosophic multi-character survey under stratified random sampling using neutrosophic fuzzy programming

An optimization model for fuzzy nonlinear programming with Beale's conditions using trapezoidal membership functions

An Interval-Valued Intuitionistic Hesitant Fuzzy Methodology and Application

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