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A hybrid multi-criteria decision-making approach for longitudinal data

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Abstract

The purpose of this paper is to propose an approach to meet the need for a robust, longitudinal, and an objective multi-criteria decision-making method. This paper presents a hybrid approach that uses time-series data to produce multi-criteria decision-making (MCDM) based future rankings of alternatives. The suggested approach leverages the strengths of existing methods such as grey forecasting for small sample prediction, Criteria Importance Through Inter-criteria Correlation (CRITIC) for objective criteria weighting, Multiplicative, Multi-Objective Optimization on the basis of Ratio Analysis (MULTIMOORA) for robust aggregation, and a combination of rank integration methods. The proposed approach is illustrated using the case of the Logistics Performance Index dataset published by the World Bank for The Organisation for Economic Co-operation and Development (OECD) countries. Further, results are compared with the aggregate ranks published by the World bank (2010–2018), and the differences are discussed. Practitioners would find the suggested approach useful because of its predictive ability, versatility, objectivity, and robustness of results. Further, the suggested approach is a useful contribution to existing research in terms of providing a MCDM method to generate future ranks.

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Availability of data and materials

The data supporting this study's findings are available on the World Bank website at https://lpi.worldbank.org/.

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Acknowledgements

We acknowledge the efforts of the reviewers in improving this manuscript with gratitude.

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Authors and Affiliations

  1. Institute of Management Technology Hyderabad, Survey No. 38, Charlaguda Village, Shamshabad Mandal, RR District, Hyderabad, Telangana, 501218, India

    Kalyana C. Chejarla

  2. Indian Institute of Management, Lucknow, India

    Omkarprasad S. Vaidya

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  1. Kalyana C. Chejarla
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  2. Omkarprasad S. Vaidya
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Chejarla, K.C., Vaidya, O.S. A hybrid multi-criteria decision-making approach for longitudinal data. OPSEARCH (2024). https://doi.org/10.1007/s12597-023-00736-y

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