Problem of Compromise Allocation in Multivariate Stratified Sampling Using Intuitionistic Fuzzy Programming

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

“…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.…”

“…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.…”

Multiobjective intuitionistic fuzzy programming under pessimistic and optimistic applications in multivariate stratified sample allocation problems

“…Alike, Raghav et al [32] explored a multiobjective mathematical programming problem to maximize the profit and minimize the cost function utilizing various methods for preventative system maintenance. Gupta et al [33] discussed the stratified random sampling with minimization of variance and solved it with IF programming. Additionally, using Pareto-based algorithms, Tirkolaee et al [34] discussed multiobjective optimization for a sustainable pollution-routing problem using cross-dock selection and found solutions using different optimization techniques.…”