A hybrid multi-criteria decision-making approach for longitudinal data
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...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Chejarla, Kalyana C. [verfasserIn] Vaidya, Omkarprasad S. [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2024 |
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| Schlagwörter: |
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| Anmerkung: |
© The Author(s), under exclusive licence to Operational Research Society of India 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
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| Übergeordnetes Werk: |
Enthalten in: Opsearch - Springer India, 1997, 61(2024), 3 vom: 04. Feb., Seite 1013-1060 |
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| Übergeordnetes Werk: |
volume:61 ; year:2024 ; number:3 ; day:04 ; month:02 ; pages:1013-1060 |
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| DOI / URN: |
10.1007/s12597-023-00736-y |
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| Katalog-ID: |
SPR05706198X |
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| 520 | |a 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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10.1007/s12597-023-00736-y doi (DE-627)SPR05706198X (SPR)s12597-023-00736-y-e DE-627 ger DE-627 rakwb eng 620 VZ 3,2 ssgn Chejarla, Kalyana C. verfasserin (orcid)0000-0002-7074-5265 aut A hybrid multi-criteria decision-making approach for longitudinal data 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Operational Research Society of India 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. 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. Grey forecasting (dpeaa)DE-He213 Objective criteria weighting (dpeaa)DE-He213 Longitudinal MCDM (dpeaa)DE-He213 Hybrid MCDM (dpeaa)DE-He213 Logistics performance (dpeaa)DE-He213 Vaidya, Omkarprasad S. verfasserin aut Enthalten in Opsearch Springer India, 1997 61(2024), 3 vom: 04. Feb., Seite 1013-1060 (DE-627)609775766 (DE-600)2516085-0 0975-0320 nnns volume:61 year:2024 number:3 day:04 month:02 pages:1013-1060 https://dx.doi.org/10.1007/s12597-023-00736-y X:SPRINGER Resolving-System lizenzpflichtig Volltext SYSFLAG_0 GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 61 2024 3 04 02 1013-1060 |
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10.1007/s12597-023-00736-y doi (DE-627)SPR05706198X (SPR)s12597-023-00736-y-e DE-627 ger DE-627 rakwb eng 620 VZ 3,2 ssgn Chejarla, Kalyana C. verfasserin (orcid)0000-0002-7074-5265 aut A hybrid multi-criteria decision-making approach for longitudinal data 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Operational Research Society of India 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. 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. Grey forecasting (dpeaa)DE-He213 Objective criteria weighting (dpeaa)DE-He213 Longitudinal MCDM (dpeaa)DE-He213 Hybrid MCDM (dpeaa)DE-He213 Logistics performance (dpeaa)DE-He213 Vaidya, Omkarprasad S. verfasserin aut Enthalten in Opsearch Springer India, 1997 61(2024), 3 vom: 04. Feb., Seite 1013-1060 (DE-627)609775766 (DE-600)2516085-0 0975-0320 nnns volume:61 year:2024 number:3 day:04 month:02 pages:1013-1060 https://dx.doi.org/10.1007/s12597-023-00736-y X:SPRINGER Resolving-System lizenzpflichtig Volltext SYSFLAG_0 GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 61 2024 3 04 02 1013-1060 |
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10.1007/s12597-023-00736-y doi (DE-627)SPR05706198X (SPR)s12597-023-00736-y-e DE-627 ger DE-627 rakwb eng 620 VZ 3,2 ssgn Chejarla, Kalyana C. verfasserin (orcid)0000-0002-7074-5265 aut A hybrid multi-criteria decision-making approach for longitudinal data 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Operational Research Society of India 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. 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. Grey forecasting (dpeaa)DE-He213 Objective criteria weighting (dpeaa)DE-He213 Longitudinal MCDM (dpeaa)DE-He213 Hybrid MCDM (dpeaa)DE-He213 Logistics performance (dpeaa)DE-He213 Vaidya, Omkarprasad S. verfasserin aut Enthalten in Opsearch Springer India, 1997 61(2024), 3 vom: 04. Feb., Seite 1013-1060 (DE-627)609775766 (DE-600)2516085-0 0975-0320 nnns volume:61 year:2024 number:3 day:04 month:02 pages:1013-1060 https://dx.doi.org/10.1007/s12597-023-00736-y X:SPRINGER Resolving-System lizenzpflichtig Volltext SYSFLAG_0 GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 61 2024 3 04 02 1013-1060 |
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10.1007/s12597-023-00736-y doi (DE-627)SPR05706198X (SPR)s12597-023-00736-y-e DE-627 ger DE-627 rakwb eng 620 VZ 3,2 ssgn Chejarla, Kalyana C. verfasserin (orcid)0000-0002-7074-5265 aut A hybrid multi-criteria decision-making approach for longitudinal data 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Operational Research Society of India 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. 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. Grey forecasting (dpeaa)DE-He213 Objective criteria weighting (dpeaa)DE-He213 Longitudinal MCDM (dpeaa)DE-He213 Hybrid MCDM (dpeaa)DE-He213 Logistics performance (dpeaa)DE-He213 Vaidya, Omkarprasad S. verfasserin aut Enthalten in Opsearch Springer India, 1997 61(2024), 3 vom: 04. Feb., Seite 1013-1060 (DE-627)609775766 (DE-600)2516085-0 0975-0320 nnns volume:61 year:2024 number:3 day:04 month:02 pages:1013-1060 https://dx.doi.org/10.1007/s12597-023-00736-y X:SPRINGER Resolving-System lizenzpflichtig Volltext SYSFLAG_0 GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 61 2024 3 04 02 1013-1060 |
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10.1007/s12597-023-00736-y doi (DE-627)SPR05706198X (SPR)s12597-023-00736-y-e DE-627 ger DE-627 rakwb eng 620 VZ 3,2 ssgn Chejarla, Kalyana C. verfasserin (orcid)0000-0002-7074-5265 aut A hybrid multi-criteria decision-making approach for longitudinal data 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Operational Research Society of India 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. 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. Grey forecasting (dpeaa)DE-He213 Objective criteria weighting (dpeaa)DE-He213 Longitudinal MCDM (dpeaa)DE-He213 Hybrid MCDM (dpeaa)DE-He213 Logistics performance (dpeaa)DE-He213 Vaidya, Omkarprasad S. verfasserin aut Enthalten in Opsearch Springer India, 1997 61(2024), 3 vom: 04. Feb., Seite 1013-1060 (DE-627)609775766 (DE-600)2516085-0 0975-0320 nnns volume:61 year:2024 number:3 day:04 month:02 pages:1013-1060 https://dx.doi.org/10.1007/s12597-023-00736-y X:SPRINGER Resolving-System lizenzpflichtig Volltext SYSFLAG_0 GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 61 2024 3 04 02 1013-1060 |
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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. © The Author(s), under exclusive licence to Operational Research Society of India 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
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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. © The Author(s), under exclusive licence to Operational Research Society of India 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
| abstract_unstemmed |
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. © The Author(s), under exclusive licence to Operational Research Society of India 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
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A hybrid multi-criteria decision-making approach for longitudinal data |
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Vaidya, Omkarprasad S. |
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10.1007/s12597-023-00736-y |
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|
| score |
7.401041 |