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A fuzzy compromise approach for solving multi-objective stratified sampling design

  • S. I : Hybridization of Neural Computing with Nature Inspired Algorithms
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Abstract

In this paper, we established a framework for finding out the optimal allocation in the multivariate stratified sample using the fuzzy compromise method. The problem of multivariate stratified sample is formulated as an all integer nonlinear programming problem, and a solution is obtained by using the criterion of “Minimizing the sum of the squares of coefficient of variation for different characteristics.” There is also a quantitative illustration being carried out to explain the statistical nature of the approaches and solved through the LINGO program. We have also studied different techniques for comparison.

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Authors and Affiliations

  1. Department of Statistics and Operations Research, Aligarh Muslim University, Aligarh, India

    Shazia Ghufran & Aquil Ahmed

  2. Jaipuria Institute of Management, Jaipur, India

    Srikant Gupta

Authors
  1. Shazia Ghufran
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  2. Srikant Gupta
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  3. Aquil Ahmed
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Correspondence to Srikant Gupta.

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Conflict of interest

The authors declare that there is no conflict of interests regarding the publication of this paper. As regards the similarity index of our paper, we have to state that most of the papers which have a similarity index of above 1% are authored by one of the co-author of this manuscript Dr. Shazia Ghufran. We feel it is natural to reproduce, with due reference, some of ones own old research work in fresh papers and it does not come in the category of ‘Plagiarism.’

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Ghufran, S., Gupta, S. & Ahmed, A. A fuzzy compromise approach for solving multi-objective stratified sampling design. Neural Comput & Applic 33, 10829–10840 (2021). https://doi.org/10.1007/s00521-020-05152-7

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  • DOI: https://doi.org/10.1007/s00521-020-05152-7

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