TOPSIS and Modified TOPSIS : a comparative analysis

Selection of an appropriate Multiple Attribute Decision Making (MADM) method for providing a solution to a given MADM problem is always challenging endeavour. The challenge is even greater for situations where for a specific MADM problem there exist multiple MADM methods with similar degree of suita...
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Autor*in:	Chakraborty, Subrata [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2022

Rechteinformationen:	Open Access Namensnennung - Nicht kommerziell - Keine Bearbeitungen 4.0 International ; CC BY-NC-ND 4.0

Schlagwörter:	TOPSIS Modified TOPSIS Euclidean distance Simulation comparison MADM Method selection

Übergeordnetes Werk:	Enthalten in: Decision analytics journal - Amsterdam : Elsevier, 2021, 2(2022) vom: März, Artikel-ID 100021
Übergeordnetes Werk:	volume:2 ; year:2022 ; month:03 ; elocationid:100021

Links:	Link aufrufen Link aufrufen

DOI / URN:	10.1016/j.dajour.2021.100021

Katalog-ID:	1796385190

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allfields	10.1016/j.dajour.2021.100021 doi (DE-627)1796385190 (DE-599)KXP1796385190 DE-627 ger DE-627 rda eng Chakraborty, Subrata verfasserin aut TOPSIS and Modified TOPSIS a comparative analysis Subrata Chakraborty 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier DE-206 Open Access Controlled Vocabulary for Access Rights http://purl.org/coar/access_right/c_abf2 Selection of an appropriate Multiple Attribute Decision Making (MADM) method for providing a solution to a given MADM problem is always challenging endeavour. The challenge is even greater for situations where for a specific MADM problem there exist multiple MADM methods with similar degree of suitability. The Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) and its dominant variant the Modified TOPSIS methods are two very similar methods applicable to the same type of MADM problems. This study provides extensive simulation-based comparisons and mathematical analysis of these two popular methods in order to clarify the confusion regarding their selection for solving MADM problems. DE-206 Namensnennung - Nicht kommerziell - Keine Bearbeitungen 4.0 International CC BY-NC-ND 4.0 cc https://creativecommons.org/licenses/by-nc-nd/4.0/ TOPSIS (dpeaa)DE-206 Modified TOPSIS (dpeaa)DE-206 Euclidean distance (dpeaa)DE-206 Simulation comparison (dpeaa)DE-206 MADM (dpeaa)DE-206 Method selection (dpeaa)DE-206 Enthalten in Decision analytics journal Amsterdam : Elsevier, 2021 2(2022) vom: März, Artikel-ID 100021 Online-Ressource (DE-627)178621072X (DE-600)3106160-6 2772-6622 nnns volume:2 year:2022 month:03 elocationid:100021 https://www.sciencedirect.com/science/article/pii/S277266222100014X/pdfft?md5=89307009f58db4d435e589113d2b5d2e&pid=1-s2.0-S277266222100014X-main.pdf Verlag kostenfrei http://doi.org/10.1016/j.dajour.2021.100021 Resolving-System kostenfrei GBV_USEFLAG_U GBV_ILN_26 ISIL_DE-206 SYSFLAG_1 GBV_KXP GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 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_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2001 GBV_ILN_2003 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_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 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_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 GBV_ILN_2403 GBV_ILN_2403 ISIL_DE-LFER AR 2 2022 3 100021 26 01 0206 409810394X x1z 23-03-22 2403 01 DE-LFER 4113036411 00 --%%-- --%%-- n --%%-- l01 07-04-22 2403 01 DE-LFER http://doi.org/10.1016/j.dajour.2021.100021 2403 01 DE-LFER https://www.sciencedirect.com/science/article/pii/S277266222100014X/pdfft?md5=89307009f58db4d435e589113d2b5d2e&pid=1-s2.0-S277266222100014X-main.pdf
spelling	10.1016/j.dajour.2021.100021 doi (DE-627)1796385190 (DE-599)KXP1796385190 DE-627 ger DE-627 rda eng Chakraborty, Subrata verfasserin aut TOPSIS and Modified TOPSIS a comparative analysis Subrata Chakraborty 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier DE-206 Open Access Controlled Vocabulary for Access Rights http://purl.org/coar/access_right/c_abf2 Selection of an appropriate Multiple Attribute Decision Making (MADM) method for providing a solution to a given MADM problem is always challenging endeavour. The challenge is even greater for situations where for a specific MADM problem there exist multiple MADM methods with similar degree of suitability. The Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) and its dominant variant the Modified TOPSIS methods are two very similar methods applicable to the same type of MADM problems. This study provides extensive simulation-based comparisons and mathematical analysis of these two popular methods in order to clarify the confusion regarding their selection for solving MADM problems. DE-206 Namensnennung - Nicht kommerziell - Keine Bearbeitungen 4.0 International CC BY-NC-ND 4.0 cc https://creativecommons.org/licenses/by-nc-nd/4.0/ TOPSIS (dpeaa)DE-206 Modified TOPSIS (dpeaa)DE-206 Euclidean distance (dpeaa)DE-206 Simulation comparison (dpeaa)DE-206 MADM (dpeaa)DE-206 Method selection (dpeaa)DE-206 Enthalten in Decision analytics journal Amsterdam : Elsevier, 2021 2(2022) vom: März, Artikel-ID 100021 Online-Ressource (DE-627)178621072X (DE-600)3106160-6 2772-6622 nnns volume:2 year:2022 month:03 elocationid:100021 https://www.sciencedirect.com/science/article/pii/S277266222100014X/pdfft?md5=89307009f58db4d435e589113d2b5d2e&pid=1-s2.0-S277266222100014X-main.pdf Verlag kostenfrei http://doi.org/10.1016/j.dajour.2021.100021 Resolving-System kostenfrei GBV_USEFLAG_U GBV_ILN_26 ISIL_DE-206 SYSFLAG_1 GBV_KXP GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 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_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2001 GBV_ILN_2003 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_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 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_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 GBV_ILN_2403 GBV_ILN_2403 ISIL_DE-LFER AR 2 2022 3 100021 26 01 0206 409810394X x1z 23-03-22 2403 01 DE-LFER 4113036411 00 --%%-- --%%-- n --%%-- l01 07-04-22 2403 01 DE-LFER http://doi.org/10.1016/j.dajour.2021.100021 2403 01 DE-LFER https://www.sciencedirect.com/science/article/pii/S277266222100014X/pdfft?md5=89307009f58db4d435e589113d2b5d2e&pid=1-s2.0-S277266222100014X-main.pdf
allfields_unstemmed	10.1016/j.dajour.2021.100021 doi (DE-627)1796385190 (DE-599)KXP1796385190 DE-627 ger DE-627 rda eng Chakraborty, Subrata verfasserin aut TOPSIS and Modified TOPSIS a comparative analysis Subrata Chakraborty 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier DE-206 Open Access Controlled Vocabulary for Access Rights http://purl.org/coar/access_right/c_abf2 Selection of an appropriate Multiple Attribute Decision Making (MADM) method for providing a solution to a given MADM problem is always challenging endeavour. The challenge is even greater for situations where for a specific MADM problem there exist multiple MADM methods with similar degree of suitability. The Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) and its dominant variant the Modified TOPSIS methods are two very similar methods applicable to the same type of MADM problems. This study provides extensive simulation-based comparisons and mathematical analysis of these two popular methods in order to clarify the confusion regarding their selection for solving MADM problems. DE-206 Namensnennung - Nicht kommerziell - Keine Bearbeitungen 4.0 International CC BY-NC-ND 4.0 cc https://creativecommons.org/licenses/by-nc-nd/4.0/ TOPSIS (dpeaa)DE-206 Modified TOPSIS (dpeaa)DE-206 Euclidean distance (dpeaa)DE-206 Simulation comparison (dpeaa)DE-206 MADM (dpeaa)DE-206 Method selection (dpeaa)DE-206 Enthalten in Decision analytics journal Amsterdam : Elsevier, 2021 2(2022) vom: März, Artikel-ID 100021 Online-Ressource (DE-627)178621072X (DE-600)3106160-6 2772-6622 nnns volume:2 year:2022 month:03 elocationid:100021 https://www.sciencedirect.com/science/article/pii/S277266222100014X/pdfft?md5=89307009f58db4d435e589113d2b5d2e&pid=1-s2.0-S277266222100014X-main.pdf Verlag kostenfrei http://doi.org/10.1016/j.dajour.2021.100021 Resolving-System kostenfrei GBV_USEFLAG_U GBV_ILN_26 ISIL_DE-206 SYSFLAG_1 GBV_KXP GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 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_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2001 GBV_ILN_2003 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_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 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_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 GBV_ILN_2403 GBV_ILN_2403 ISIL_DE-LFER AR 2 2022 3 100021 26 01 0206 409810394X x1z 23-03-22 2403 01 DE-LFER 4113036411 00 --%%-- --%%-- n --%%-- l01 07-04-22 2403 01 DE-LFER http://doi.org/10.1016/j.dajour.2021.100021 2403 01 DE-LFER https://www.sciencedirect.com/science/article/pii/S277266222100014X/pdfft?md5=89307009f58db4d435e589113d2b5d2e&pid=1-s2.0-S277266222100014X-main.pdf
allfieldsGer	10.1016/j.dajour.2021.100021 doi (DE-627)1796385190 (DE-599)KXP1796385190 DE-627 ger DE-627 rda eng Chakraborty, Subrata verfasserin aut TOPSIS and Modified TOPSIS a comparative analysis Subrata Chakraborty 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier DE-206 Open Access Controlled Vocabulary for Access Rights http://purl.org/coar/access_right/c_abf2 Selection of an appropriate Multiple Attribute Decision Making (MADM) method for providing a solution to a given MADM problem is always challenging endeavour. The challenge is even greater for situations where for a specific MADM problem there exist multiple MADM methods with similar degree of suitability. The Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) and its dominant variant the Modified TOPSIS methods are two very similar methods applicable to the same type of MADM problems. This study provides extensive simulation-based comparisons and mathematical analysis of these two popular methods in order to clarify the confusion regarding their selection for solving MADM problems. DE-206 Namensnennung - Nicht kommerziell - Keine Bearbeitungen 4.0 International CC BY-NC-ND 4.0 cc https://creativecommons.org/licenses/by-nc-nd/4.0/ TOPSIS (dpeaa)DE-206 Modified TOPSIS (dpeaa)DE-206 Euclidean distance (dpeaa)DE-206 Simulation comparison (dpeaa)DE-206 MADM (dpeaa)DE-206 Method selection (dpeaa)DE-206 Enthalten in Decision analytics journal Amsterdam : Elsevier, 2021 2(2022) vom: März, Artikel-ID 100021 Online-Ressource (DE-627)178621072X (DE-600)3106160-6 2772-6622 nnns volume:2 year:2022 month:03 elocationid:100021 https://www.sciencedirect.com/science/article/pii/S277266222100014X/pdfft?md5=89307009f58db4d435e589113d2b5d2e&pid=1-s2.0-S277266222100014X-main.pdf Verlag kostenfrei http://doi.org/10.1016/j.dajour.2021.100021 Resolving-System kostenfrei GBV_USEFLAG_U GBV_ILN_26 ISIL_DE-206 SYSFLAG_1 GBV_KXP GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 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_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2001 GBV_ILN_2003 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_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 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_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 GBV_ILN_2403 GBV_ILN_2403 ISIL_DE-LFER AR 2 2022 3 100021 26 01 0206 409810394X x1z 23-03-22 2403 01 DE-LFER 4113036411 00 --%%-- --%%-- n --%%-- l01 07-04-22 2403 01 DE-LFER http://doi.org/10.1016/j.dajour.2021.100021 2403 01 DE-LFER https://www.sciencedirect.com/science/article/pii/S277266222100014X/pdfft?md5=89307009f58db4d435e589113d2b5d2e&pid=1-s2.0-S277266222100014X-main.pdf
allfieldsSound	10.1016/j.dajour.2021.100021 doi (DE-627)1796385190 (DE-599)KXP1796385190 DE-627 ger DE-627 rda eng Chakraborty, Subrata verfasserin aut TOPSIS and Modified TOPSIS a comparative analysis Subrata Chakraborty 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier DE-206 Open Access Controlled Vocabulary for Access Rights http://purl.org/coar/access_right/c_abf2 Selection of an appropriate Multiple Attribute Decision Making (MADM) method for providing a solution to a given MADM problem is always challenging endeavour. The challenge is even greater for situations where for a specific MADM problem there exist multiple MADM methods with similar degree of suitability. The Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) and its dominant variant the Modified TOPSIS methods are two very similar methods applicable to the same type of MADM problems. This study provides extensive simulation-based comparisons and mathematical analysis of these two popular methods in order to clarify the confusion regarding their selection for solving MADM problems. DE-206 Namensnennung - Nicht kommerziell - Keine Bearbeitungen 4.0 International CC BY-NC-ND 4.0 cc https://creativecommons.org/licenses/by-nc-nd/4.0/ TOPSIS (dpeaa)DE-206 Modified TOPSIS (dpeaa)DE-206 Euclidean distance (dpeaa)DE-206 Simulation comparison (dpeaa)DE-206 MADM (dpeaa)DE-206 Method selection (dpeaa)DE-206 Enthalten in Decision analytics journal Amsterdam : Elsevier, 2021 2(2022) vom: März, Artikel-ID 100021 Online-Ressource (DE-627)178621072X (DE-600)3106160-6 2772-6622 nnns volume:2 year:2022 month:03 elocationid:100021 https://www.sciencedirect.com/science/article/pii/S277266222100014X/pdfft?md5=89307009f58db4d435e589113d2b5d2e&pid=1-s2.0-S277266222100014X-main.pdf Verlag kostenfrei http://doi.org/10.1016/j.dajour.2021.100021 Resolving-System kostenfrei GBV_USEFLAG_U GBV_ILN_26 ISIL_DE-206 SYSFLAG_1 GBV_KXP GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 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_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2001 GBV_ILN_2003 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_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 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_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 GBV_ILN_2403 GBV_ILN_2403 ISIL_DE-LFER AR 2 2022 3 100021 26 01 0206 409810394X x1z 23-03-22 2403 01 DE-LFER 4113036411 00 --%%-- --%%-- n --%%-- l01 07-04-22 2403 01 DE-LFER http://doi.org/10.1016/j.dajour.2021.100021 2403 01 DE-LFER https://www.sciencedirect.com/science/article/pii/S277266222100014X/pdfft?md5=89307009f58db4d435e589113d2b5d2e&pid=1-s2.0-S277266222100014X-main.pdf
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author	Chakraborty, Subrata
spellingShingle	Chakraborty, Subrata misc TOPSIS misc Modified TOPSIS misc Euclidean distance misc Simulation comparison misc MADM misc Method selection TOPSIS and Modified TOPSIS a comparative analysis
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topic_title	TOPSIS and Modified TOPSIS a comparative analysis Subrata Chakraborty TOPSIS (dpeaa)DE-206 Modified TOPSIS (dpeaa)DE-206 Euclidean distance (dpeaa)DE-206 Simulation comparison (dpeaa)DE-206 MADM (dpeaa)DE-206 Method selection (dpeaa)DE-206
topic	misc TOPSIS misc Modified TOPSIS misc Euclidean distance misc Simulation comparison misc MADM misc Method selection
topic_unstemmed	misc TOPSIS misc Modified TOPSIS misc Euclidean distance misc Simulation comparison misc MADM misc Method selection
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title	TOPSIS and Modified TOPSIS a comparative analysis
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title_full	TOPSIS and Modified TOPSIS a comparative analysis Subrata Chakraborty
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title_sub	a comparative analysis
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title_sort	topsis and modified topsisa comparative analysis
title_auth	TOPSIS and Modified TOPSIS a comparative analysis
abstract	Selection of an appropriate Multiple Attribute Decision Making (MADM) method for providing a solution to a given MADM problem is always challenging endeavour. The challenge is even greater for situations where for a specific MADM problem there exist multiple MADM methods with similar degree of suitability. The Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) and its dominant variant the Modified TOPSIS methods are two very similar methods applicable to the same type of MADM problems. This study provides extensive simulation-based comparisons and mathematical analysis of these two popular methods in order to clarify the confusion regarding their selection for solving MADM problems.
abstractGer	Selection of an appropriate Multiple Attribute Decision Making (MADM) method for providing a solution to a given MADM problem is always challenging endeavour. The challenge is even greater for situations where for a specific MADM problem there exist multiple MADM methods with similar degree of suitability. The Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) and its dominant variant the Modified TOPSIS methods are two very similar methods applicable to the same type of MADM problems. This study provides extensive simulation-based comparisons and mathematical analysis of these two popular methods in order to clarify the confusion regarding their selection for solving MADM problems.
abstract_unstemmed	Selection of an appropriate Multiple Attribute Decision Making (MADM) method for providing a solution to a given MADM problem is always challenging endeavour. The challenge is even greater for situations where for a specific MADM problem there exist multiple MADM methods with similar degree of suitability. The Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) and its dominant variant the Modified TOPSIS methods are two very similar methods applicable to the same type of MADM problems. This study provides extensive simulation-based comparisons and mathematical analysis of these two popular methods in order to clarify the confusion regarding their selection for solving MADM problems.
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title_short	TOPSIS and Modified TOPSIS
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