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Variable Neighborhood Search Algorithm to Achieve Optimal Allocation Design for Known Covariates

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  • Samrat Hore

    (Department of Statistics, Tripura University, India)

Abstract

For obtaining an optimal allocation design of the experimental units with known single or multiple covariates into several treatment groups, has wide applications in the field of agricultural research, clinical trial, medical experiments, chemical industry, etc with a de-tailed reference is available in [1,2]. Such allocation design has been structured in the linear analysis of covariate (ANCOVA)-one way model.

Suggested Citation

  • Samrat Hore, 2017. "Variable Neighborhood Search Algorithm to Achieve Optimal Allocation Design for Known Covariates," Biostatistics and Biometrics Open Access Journal, Juniper Publishers Inc., vol. 4(1), pages 4-5, November.
    • Handle: RePEc:adp:jbboaj:v:4:y:2017:i:1:p:4-5
    DOI: 10.19080/BBOAJ.2017.04.555627
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    References listed on IDEAS

    as
    1. Hansen, Pierre & Mladenovic, Nenad, 2001. "Variable neighborhood search: Principles and applications," European Journal of Operational Research, Elsevier, vol. 130(3), pages 449-467, May.
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    Cited by:

    1. Bimal Shil & Prasenjit Sinha & Binod Chandra Tripathy & Suman Das, 2023. "VNS Based MADM-Strategy Under Possibility Environment," Annals of Data Science, Springer, vol. 10(6), pages 1571-1582, December.

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