Algorithm for bi-level multi-objective fully quadratic fractional optimization model with fuzzy parameters
Abstract The presented research is concerned to obtain an efficient solution for bi-level multi-objective fully quadratic fractional optimization model (BLMOFQFOM). It is based on the idea of transforming the fractional model into non-fractional one for the initial guess of variables. Trapezoidal fu...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Rani, Namrata [verfasserIn] Goyal, Vandana [verfasserIn] Gupta, Deepak [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2021 |
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| Schlagwörter: |
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| Anmerkung: |
© The Author(s), under exclusive licence to Springer-Verlag GmbH, DE part of Springer Nature 2021 |
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| Übergeordnetes Werk: |
Enthalten in: Journal of ambient intelligence and humanized computing - Berlin : Springer, 2010, 12(2021), 12 vom: 30. Juni, Seite 10637-10649 |
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| Übergeordnetes Werk: |
volume:12 ; year:2021 ; number:12 ; day:30 ; month:06 ; pages:10637-10649 |
| Links: |
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| DOI / URN: |
10.1007/s12652-020-02877-0 |
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| Katalog-ID: |
SPR045348391 |
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| 245 | 1 | 0 | |a Algorithm for bi-level multi-objective fully quadratic fractional optimization model with fuzzy parameters |
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| 520 | |a Abstract The presented research is concerned to obtain an efficient solution for bi-level multi-objective fully quadratic fractional optimization model (BLMOFQFOM). It is based on the idea of transforming the fractional model into non-fractional one for the initial guess of variables. Trapezoidal fuzzy numbers (TrFN) are being taken as the parameters in the fuzzy model to deal with vague and incomplete data and %$ \alpha %$-cut set is used to get a deterministic model from the fuzzy model. Concept of fuzzy goals is implemented to set aspiration level as unity for all objective functions. An attractive flowchart, algorithm and a computational problem are also illustrated to show the validity of the designed approach. | ||
| 650 | 4 | |a Aspiration level |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Bi level multi-objective optimization model (BLMOOM) |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Efficient solution |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Fuzzy models |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Multi-objective model |7 (dpeaa)DE-He213 | |
| 700 | 1 | |a Goyal, Vandana |e verfasserin |4 aut | |
| 700 | 1 | |a Gupta, Deepak |e verfasserin |4 aut | |
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10.1007/s12652-020-02877-0 doi (DE-627)SPR045348391 (SPR)s12652-020-02877-0-e DE-627 ger DE-627 rakwb eng 004 ASE 54.72 bkl 54.21 bkl Rani, Namrata verfasserin aut Algorithm for bi-level multi-objective fully quadratic fractional optimization model with fuzzy parameters 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH, DE part of Springer Nature 2021 Abstract The presented research is concerned to obtain an efficient solution for bi-level multi-objective fully quadratic fractional optimization model (BLMOFQFOM). It is based on the idea of transforming the fractional model into non-fractional one for the initial guess of variables. Trapezoidal fuzzy numbers (TrFN) are being taken as the parameters in the fuzzy model to deal with vague and incomplete data and %$ \alpha %$-cut set is used to get a deterministic model from the fuzzy model. Concept of fuzzy goals is implemented to set aspiration level as unity for all objective functions. An attractive flowchart, algorithm and a computational problem are also illustrated to show the validity of the designed approach. Aspiration level (dpeaa)DE-He213 Bi level multi-objective optimization model (BLMOOM) (dpeaa)DE-He213 Efficient solution (dpeaa)DE-He213 Fuzzy models (dpeaa)DE-He213 Multi-objective model (dpeaa)DE-He213 Goyal, Vandana verfasserin aut Gupta, Deepak verfasserin aut Enthalten in Journal of ambient intelligence and humanized computing Berlin : Springer, 2010 12(2021), 12 vom: 30. Juni, Seite 10637-10649 (DE-627)620775734 (DE-600)2543187-0 1868-5145 nnns volume:12 year:2021 number:12 day:30 month:06 pages:10637-10649 https://dx.doi.org/10.1007/s12652-020-02877-0 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A 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_101 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_2008 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_2119 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_4277 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 54.72 ASE 54.21 ASE AR 12 2021 12 30 06 10637-10649 |
| spelling |
10.1007/s12652-020-02877-0 doi (DE-627)SPR045348391 (SPR)s12652-020-02877-0-e DE-627 ger DE-627 rakwb eng 004 ASE 54.72 bkl 54.21 bkl Rani, Namrata verfasserin aut Algorithm for bi-level multi-objective fully quadratic fractional optimization model with fuzzy parameters 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH, DE part of Springer Nature 2021 Abstract The presented research is concerned to obtain an efficient solution for bi-level multi-objective fully quadratic fractional optimization model (BLMOFQFOM). It is based on the idea of transforming the fractional model into non-fractional one for the initial guess of variables. Trapezoidal fuzzy numbers (TrFN) are being taken as the parameters in the fuzzy model to deal with vague and incomplete data and %$ \alpha %$-cut set is used to get a deterministic model from the fuzzy model. Concept of fuzzy goals is implemented to set aspiration level as unity for all objective functions. An attractive flowchart, algorithm and a computational problem are also illustrated to show the validity of the designed approach. Aspiration level (dpeaa)DE-He213 Bi level multi-objective optimization model (BLMOOM) (dpeaa)DE-He213 Efficient solution (dpeaa)DE-He213 Fuzzy models (dpeaa)DE-He213 Multi-objective model (dpeaa)DE-He213 Goyal, Vandana verfasserin aut Gupta, Deepak verfasserin aut Enthalten in Journal of ambient intelligence and humanized computing Berlin : Springer, 2010 12(2021), 12 vom: 30. Juni, Seite 10637-10649 (DE-627)620775734 (DE-600)2543187-0 1868-5145 nnns volume:12 year:2021 number:12 day:30 month:06 pages:10637-10649 https://dx.doi.org/10.1007/s12652-020-02877-0 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A 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_101 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_2008 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_2119 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_4277 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 54.72 ASE 54.21 ASE AR 12 2021 12 30 06 10637-10649 |
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10.1007/s12652-020-02877-0 doi (DE-627)SPR045348391 (SPR)s12652-020-02877-0-e DE-627 ger DE-627 rakwb eng 004 ASE 54.72 bkl 54.21 bkl Rani, Namrata verfasserin aut Algorithm for bi-level multi-objective fully quadratic fractional optimization model with fuzzy parameters 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH, DE part of Springer Nature 2021 Abstract The presented research is concerned to obtain an efficient solution for bi-level multi-objective fully quadratic fractional optimization model (BLMOFQFOM). It is based on the idea of transforming the fractional model into non-fractional one for the initial guess of variables. Trapezoidal fuzzy numbers (TrFN) are being taken as the parameters in the fuzzy model to deal with vague and incomplete data and %$ \alpha %$-cut set is used to get a deterministic model from the fuzzy model. Concept of fuzzy goals is implemented to set aspiration level as unity for all objective functions. An attractive flowchart, algorithm and a computational problem are also illustrated to show the validity of the designed approach. Aspiration level (dpeaa)DE-He213 Bi level multi-objective optimization model (BLMOOM) (dpeaa)DE-He213 Efficient solution (dpeaa)DE-He213 Fuzzy models (dpeaa)DE-He213 Multi-objective model (dpeaa)DE-He213 Goyal, Vandana verfasserin aut Gupta, Deepak verfasserin aut Enthalten in Journal of ambient intelligence and humanized computing Berlin : Springer, 2010 12(2021), 12 vom: 30. Juni, Seite 10637-10649 (DE-627)620775734 (DE-600)2543187-0 1868-5145 nnns volume:12 year:2021 number:12 day:30 month:06 pages:10637-10649 https://dx.doi.org/10.1007/s12652-020-02877-0 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A 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_101 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_2008 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_2119 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_4277 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 54.72 ASE 54.21 ASE AR 12 2021 12 30 06 10637-10649 |
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10.1007/s12652-020-02877-0 doi (DE-627)SPR045348391 (SPR)s12652-020-02877-0-e DE-627 ger DE-627 rakwb eng 004 ASE 54.72 bkl 54.21 bkl Rani, Namrata verfasserin aut Algorithm for bi-level multi-objective fully quadratic fractional optimization model with fuzzy parameters 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH, DE part of Springer Nature 2021 Abstract The presented research is concerned to obtain an efficient solution for bi-level multi-objective fully quadratic fractional optimization model (BLMOFQFOM). It is based on the idea of transforming the fractional model into non-fractional one for the initial guess of variables. Trapezoidal fuzzy numbers (TrFN) are being taken as the parameters in the fuzzy model to deal with vague and incomplete data and %$ \alpha %$-cut set is used to get a deterministic model from the fuzzy model. Concept of fuzzy goals is implemented to set aspiration level as unity for all objective functions. An attractive flowchart, algorithm and a computational problem are also illustrated to show the validity of the designed approach. Aspiration level (dpeaa)DE-He213 Bi level multi-objective optimization model (BLMOOM) (dpeaa)DE-He213 Efficient solution (dpeaa)DE-He213 Fuzzy models (dpeaa)DE-He213 Multi-objective model (dpeaa)DE-He213 Goyal, Vandana verfasserin aut Gupta, Deepak verfasserin aut Enthalten in Journal of ambient intelligence and humanized computing Berlin : Springer, 2010 12(2021), 12 vom: 30. Juni, Seite 10637-10649 (DE-627)620775734 (DE-600)2543187-0 1868-5145 nnns volume:12 year:2021 number:12 day:30 month:06 pages:10637-10649 https://dx.doi.org/10.1007/s12652-020-02877-0 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A 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_101 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_2008 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_2119 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_4277 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 54.72 ASE 54.21 ASE AR 12 2021 12 30 06 10637-10649 |
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10.1007/s12652-020-02877-0 doi (DE-627)SPR045348391 (SPR)s12652-020-02877-0-e DE-627 ger DE-627 rakwb eng 004 ASE 54.72 bkl 54.21 bkl Rani, Namrata verfasserin aut Algorithm for bi-level multi-objective fully quadratic fractional optimization model with fuzzy parameters 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH, DE part of Springer Nature 2021 Abstract The presented research is concerned to obtain an efficient solution for bi-level multi-objective fully quadratic fractional optimization model (BLMOFQFOM). It is based on the idea of transforming the fractional model into non-fractional one for the initial guess of variables. Trapezoidal fuzzy numbers (TrFN) are being taken as the parameters in the fuzzy model to deal with vague and incomplete data and %$ \alpha %$-cut set is used to get a deterministic model from the fuzzy model. Concept of fuzzy goals is implemented to set aspiration level as unity for all objective functions. An attractive flowchart, algorithm and a computational problem are also illustrated to show the validity of the designed approach. Aspiration level (dpeaa)DE-He213 Bi level multi-objective optimization model (BLMOOM) (dpeaa)DE-He213 Efficient solution (dpeaa)DE-He213 Fuzzy models (dpeaa)DE-He213 Multi-objective model (dpeaa)DE-He213 Goyal, Vandana verfasserin aut Gupta, Deepak verfasserin aut Enthalten in Journal of ambient intelligence and humanized computing Berlin : Springer, 2010 12(2021), 12 vom: 30. Juni, Seite 10637-10649 (DE-627)620775734 (DE-600)2543187-0 1868-5145 nnns volume:12 year:2021 number:12 day:30 month:06 pages:10637-10649 https://dx.doi.org/10.1007/s12652-020-02877-0 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A 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_101 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_2008 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_2119 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_4277 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 54.72 ASE 54.21 ASE AR 12 2021 12 30 06 10637-10649 |
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Rani, Namrata |
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algorithm for bi-level multi-objective fully quadratic fractional optimization model with fuzzy parameters |
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Algorithm for bi-level multi-objective fully quadratic fractional optimization model with fuzzy parameters |
| abstract |
Abstract The presented research is concerned to obtain an efficient solution for bi-level multi-objective fully quadratic fractional optimization model (BLMOFQFOM). It is based on the idea of transforming the fractional model into non-fractional one for the initial guess of variables. Trapezoidal fuzzy numbers (TrFN) are being taken as the parameters in the fuzzy model to deal with vague and incomplete data and %$ \alpha %$-cut set is used to get a deterministic model from the fuzzy model. Concept of fuzzy goals is implemented to set aspiration level as unity for all objective functions. An attractive flowchart, algorithm and a computational problem are also illustrated to show the validity of the designed approach. © The Author(s), under exclusive licence to Springer-Verlag GmbH, DE part of Springer Nature 2021 |
| abstractGer |
Abstract The presented research is concerned to obtain an efficient solution for bi-level multi-objective fully quadratic fractional optimization model (BLMOFQFOM). It is based on the idea of transforming the fractional model into non-fractional one for the initial guess of variables. Trapezoidal fuzzy numbers (TrFN) are being taken as the parameters in the fuzzy model to deal with vague and incomplete data and %$ \alpha %$-cut set is used to get a deterministic model from the fuzzy model. Concept of fuzzy goals is implemented to set aspiration level as unity for all objective functions. An attractive flowchart, algorithm and a computational problem are also illustrated to show the validity of the designed approach. © The Author(s), under exclusive licence to Springer-Verlag GmbH, DE part of Springer Nature 2021 |
| abstract_unstemmed |
Abstract The presented research is concerned to obtain an efficient solution for bi-level multi-objective fully quadratic fractional optimization model (BLMOFQFOM). It is based on the idea of transforming the fractional model into non-fractional one for the initial guess of variables. Trapezoidal fuzzy numbers (TrFN) are being taken as the parameters in the fuzzy model to deal with vague and incomplete data and %$ \alpha %$-cut set is used to get a deterministic model from the fuzzy model. Concept of fuzzy goals is implemented to set aspiration level as unity for all objective functions. An attractive flowchart, algorithm and a computational problem are also illustrated to show the validity of the designed approach. © The Author(s), under exclusive licence to Springer-Verlag GmbH, DE part of Springer Nature 2021 |
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Algorithm for bi-level multi-objective fully quadratic fractional optimization model with fuzzy parameters |
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https://dx.doi.org/10.1007/s12652-020-02877-0 |
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Goyal, Vandana Gupta, Deepak |
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| doi_str |
10.1007/s12652-020-02877-0 |
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2024-07-03T15:25:02.878Z |
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| score |
7.401374 |