Compromise allocation problem in multivariate stratified sampling with flexible fuzzy goals

Haq, Ahteshamul; Ali, Irfan; Varshney, Rahul

doi:10.1080/00949655.2020.1734808

Cited by 8 publications

(3 citation statements)

References 14 publications

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“…Tariq et al [31] used fuzzy geometric programming to solve the optimal allocation problem in two-phase multivariate stratified sampling under the classical linear cost function. Haq et al [15] , Ahmadini et al [2] , Khanam et al [19] and Jalil et al [16] applied fuzzy optimization methods to find integer compromise allocation for estimating population mean under classical linear and non linear cost functions, considering measurement unit cost, labor cost, and traveling cost in multivariate stratified sampling. Gupta et al [14] and Raghav et al [24] proposed intuitionistic fuzzy programming methods to solve the multi-objective integer optimum allocation problem, estimating the population mean of multiple variables under the study.…”

Section: Introductionmentioning

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: Introductionmentioning

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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“…e authors suggested the use of coefficient of variations instead of variances. Also, the MSS problem has been studied with stochastic optimal design [15,16], with flexible goals [17], and with integer solution [18].…”

Section: Introductionmentioning

confidence: 99%

Nonlinear Stochastic Multiobjective Optimization Problem in Multivariate Stratified Sampling Design

Alshqaq

Ahmadini

Ali

2022

Mathematical Problems in Engineering

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Decision-making in survey sampling planning is a tricky situation; sometimes it involves multiple objectives, with various decision variables emanating from heterogeneous and homogeneous populations. Dealing with the entire population under study and its uncertain nature becomes a challenging issue for researchers and policymakers. Hence, an appropriate sampling design and optimization methodology are imperative. The study presents a useful discussion on stochastic multiobjective multivariate stratified sampling (MSS) models theoretically, and the concepts are illustrated with numerical examples. Also, it has been found that the linearization of sampling variance in survey sampling does not help determine the optimal sampling allocation problem with minimum variability. Optimal allocation problems under the weighted goal programming, stochastic goal programming, and Chebyshev goal programming methods are also discussed with numerical examples. Finally, the study discussed the linear approximation of the MSS problem with examples. The study is a conceptual and theoretical framework for MSS under a stochastic environment. The numerical data is simulated using the stratifyR package.

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“…Chang [38] discussed the mixed binary problem with GP; Ramzannia et al [39] presented flexible fuzzy goals (FFGs) and constraints for multichoice goal programming. Haq et al [40] described how to convert a nonlinear programming problem into a binary goal programming (BGP) problem and solve it using the FFG programming approach.…”

Section: Introductionmentioning

confidence: 99%

Flexible fractional transportation problem with multiple goals: a pentagonal fuzzy concept

2022

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We present the framework of the multiobjective fractional transportation problem in the form of pentagonal fuzzy supply and demand. The ideal transportation model is set up to match the decision makers' preferences in competing for the criteria, and transportation costs, delivery time, degradation, environmental and social concerns are the objectives. We employed flexible fuzzy goal programming to handle the Model's complexity to improve the reasonable compromise. The real-world problem of wind turbine blades is used to validate the superiority and effectiveness of the technique.

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Compromise allocation problem in multivariate stratified sampling with flexible fuzzy goals

Cited by 8 publications

References 14 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

Nonlinear Stochastic Multiobjective Optimization Problem in Multivariate Stratified Sampling Design

Flexible fractional transportation problem with multiple goals: a pentagonal fuzzy concept

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