Modality of teaching learning based optimization algorithm to reduce the consistency ratio of the pair-wise comparison matrix in analytical hierarchy processing

Abstract This paper presents an approach to improve the consistency of pair-wise comparison matrix in analytical hierarchy process (AHP) using teaching learning based optimization (TLBO) algorithm. The purpose of this proposed approach to minimize the consistency ratio (CR). Consistency test for the...
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Autor*in:	Borkar, Prashant [verfasserIn] Sarode, M. V. [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2017

Schlagwörter:	Analytical hierarchy process (AHP) Pair-wise comparison matrix Teaching learning based optimization (TLBO) Consistency ratio

Übergeordnetes Werk:	Enthalten in: Evolving systems - Berlin : Springer, 2010, 9(2017), 2 vom: 02. Mai, Seite 169-180
Übergeordnetes Werk:	volume:9 ; year:2017 ; number:2 ; day:02 ; month:05 ; pages:169-180

Links:	Volltext

DOI / URN:	10.1007/s12530-017-9185-9

Katalog-ID:	SPR026053128

Internformat


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520			\|a Abstract This paper presents an approach to improve the consistency of pair-wise comparison matrix in analytical hierarchy process (AHP) using teaching learning based optimization (TLBO) algorithm. The purpose of this proposed approach to minimize the consistency ratio (CR). Consistency test for the comparison matrix in AHP have been studied rigorously since AHP was introduced in 1970s. However, existing approaches are either too complicated or difficult. Most of them could not preserve the original judgments provided by an expert. To improve the consistency ratio (CR), this research work proposes a simple, effective and efficient method which will minimize the CR to almost zero while preserving the judgment values in pair-wise comparison matrix. The correctness of the proposed method is proved by applying it to two real world case studies reported in literature, namely new product design selection and material selection (work tool combination). The experimentation shows that the proposed approach is efficient and accurate to satisfy the consistency requirements of AHP.
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700	1		\|a Sarode, M. V. \|e verfasserin \|4 aut
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matchkey_str	article:18686486:2017----::oaiyfecigerigaeotmztoagrtmoeuehcnitnyaiotearieoprsn
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allfields	10.1007/s12530-017-9185-9 doi (DE-627)SPR026053128 (SPR)s12530-017-9185-9-e DE-627 ger DE-627 rakwb eng 004 ASE 30.10 bkl 54.72 bkl Borkar, Prashant verfasserin aut Modality of teaching learning based optimization algorithm to reduce the consistency ratio of the pair-wise comparison matrix in analytical hierarchy processing 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract This paper presents an approach to improve the consistency of pair-wise comparison matrix in analytical hierarchy process (AHP) using teaching learning based optimization (TLBO) algorithm. The purpose of this proposed approach to minimize the consistency ratio (CR). Consistency test for the comparison matrix in AHP have been studied rigorously since AHP was introduced in 1970s. However, existing approaches are either too complicated or difficult. Most of them could not preserve the original judgments provided by an expert. To improve the consistency ratio (CR), this research work proposes a simple, effective and efficient method which will minimize the CR to almost zero while preserving the judgment values in pair-wise comparison matrix. The correctness of the proposed method is proved by applying it to two real world case studies reported in literature, namely new product design selection and material selection (work tool combination). The experimentation shows that the proposed approach is efficient and accurate to satisfy the consistency requirements of AHP. Analytical hierarchy process (AHP) (dpeaa)DE-He213 Pair-wise comparison matrix (dpeaa)DE-He213 Teaching learning based optimization (TLBO) (dpeaa)DE-He213 Consistency ratio (dpeaa)DE-He213 Sarode, M. V. verfasserin aut Enthalten in Evolving systems Berlin : Springer, 2010 9(2017), 2 vom: 02. Mai, Seite 169-180 (DE-627)630795533 (DE-600)2561630-4 1868-6486 nnns volume:9 year:2017 number:2 day:02 month:05 pages:169-180 https://dx.doi.org/10.1007/s12530-017-9185-9 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_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_266 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_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 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_2116 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_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_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 30.10 ASE 54.72 ASE AR 9 2017 2 02 05 169-180
spelling	10.1007/s12530-017-9185-9 doi (DE-627)SPR026053128 (SPR)s12530-017-9185-9-e DE-627 ger DE-627 rakwb eng 004 ASE 30.10 bkl 54.72 bkl Borkar, Prashant verfasserin aut Modality of teaching learning based optimization algorithm to reduce the consistency ratio of the pair-wise comparison matrix in analytical hierarchy processing 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract This paper presents an approach to improve the consistency of pair-wise comparison matrix in analytical hierarchy process (AHP) using teaching learning based optimization (TLBO) algorithm. The purpose of this proposed approach to minimize the consistency ratio (CR). Consistency test for the comparison matrix in AHP have been studied rigorously since AHP was introduced in 1970s. However, existing approaches are either too complicated or difficult. Most of them could not preserve the original judgments provided by an expert. To improve the consistency ratio (CR), this research work proposes a simple, effective and efficient method which will minimize the CR to almost zero while preserving the judgment values in pair-wise comparison matrix. The correctness of the proposed method is proved by applying it to two real world case studies reported in literature, namely new product design selection and material selection (work tool combination). The experimentation shows that the proposed approach is efficient and accurate to satisfy the consistency requirements of AHP. Analytical hierarchy process (AHP) (dpeaa)DE-He213 Pair-wise comparison matrix (dpeaa)DE-He213 Teaching learning based optimization (TLBO) (dpeaa)DE-He213 Consistency ratio (dpeaa)DE-He213 Sarode, M. V. verfasserin aut Enthalten in Evolving systems Berlin : Springer, 2010 9(2017), 2 vom: 02. Mai, Seite 169-180 (DE-627)630795533 (DE-600)2561630-4 1868-6486 nnns volume:9 year:2017 number:2 day:02 month:05 pages:169-180 https://dx.doi.org/10.1007/s12530-017-9185-9 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_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_266 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_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 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_2116 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_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_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 30.10 ASE 54.72 ASE AR 9 2017 2 02 05 169-180
allfields_unstemmed	10.1007/s12530-017-9185-9 doi (DE-627)SPR026053128 (SPR)s12530-017-9185-9-e DE-627 ger DE-627 rakwb eng 004 ASE 30.10 bkl 54.72 bkl Borkar, Prashant verfasserin aut Modality of teaching learning based optimization algorithm to reduce the consistency ratio of the pair-wise comparison matrix in analytical hierarchy processing 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract This paper presents an approach to improve the consistency of pair-wise comparison matrix in analytical hierarchy process (AHP) using teaching learning based optimization (TLBO) algorithm. The purpose of this proposed approach to minimize the consistency ratio (CR). Consistency test for the comparison matrix in AHP have been studied rigorously since AHP was introduced in 1970s. However, existing approaches are either too complicated or difficult. Most of them could not preserve the original judgments provided by an expert. To improve the consistency ratio (CR), this research work proposes a simple, effective and efficient method which will minimize the CR to almost zero while preserving the judgment values in pair-wise comparison matrix. The correctness of the proposed method is proved by applying it to two real world case studies reported in literature, namely new product design selection and material selection (work tool combination). The experimentation shows that the proposed approach is efficient and accurate to satisfy the consistency requirements of AHP. Analytical hierarchy process (AHP) (dpeaa)DE-He213 Pair-wise comparison matrix (dpeaa)DE-He213 Teaching learning based optimization (TLBO) (dpeaa)DE-He213 Consistency ratio (dpeaa)DE-He213 Sarode, M. V. verfasserin aut Enthalten in Evolving systems Berlin : Springer, 2010 9(2017), 2 vom: 02. Mai, Seite 169-180 (DE-627)630795533 (DE-600)2561630-4 1868-6486 nnns volume:9 year:2017 number:2 day:02 month:05 pages:169-180 https://dx.doi.org/10.1007/s12530-017-9185-9 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_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_266 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_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 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_2116 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_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_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 30.10 ASE 54.72 ASE AR 9 2017 2 02 05 169-180
allfieldsGer	10.1007/s12530-017-9185-9 doi (DE-627)SPR026053128 (SPR)s12530-017-9185-9-e DE-627 ger DE-627 rakwb eng 004 ASE 30.10 bkl 54.72 bkl Borkar, Prashant verfasserin aut Modality of teaching learning based optimization algorithm to reduce the consistency ratio of the pair-wise comparison matrix in analytical hierarchy processing 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract This paper presents an approach to improve the consistency of pair-wise comparison matrix in analytical hierarchy process (AHP) using teaching learning based optimization (TLBO) algorithm. The purpose of this proposed approach to minimize the consistency ratio (CR). Consistency test for the comparison matrix in AHP have been studied rigorously since AHP was introduced in 1970s. However, existing approaches are either too complicated or difficult. Most of them could not preserve the original judgments provided by an expert. To improve the consistency ratio (CR), this research work proposes a simple, effective and efficient method which will minimize the CR to almost zero while preserving the judgment values in pair-wise comparison matrix. The correctness of the proposed method is proved by applying it to two real world case studies reported in literature, namely new product design selection and material selection (work tool combination). The experimentation shows that the proposed approach is efficient and accurate to satisfy the consistency requirements of AHP. Analytical hierarchy process (AHP) (dpeaa)DE-He213 Pair-wise comparison matrix (dpeaa)DE-He213 Teaching learning based optimization (TLBO) (dpeaa)DE-He213 Consistency ratio (dpeaa)DE-He213 Sarode, M. V. verfasserin aut Enthalten in Evolving systems Berlin : Springer, 2010 9(2017), 2 vom: 02. Mai, Seite 169-180 (DE-627)630795533 (DE-600)2561630-4 1868-6486 nnns volume:9 year:2017 number:2 day:02 month:05 pages:169-180 https://dx.doi.org/10.1007/s12530-017-9185-9 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_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_266 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_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 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_2116 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_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_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 30.10 ASE 54.72 ASE AR 9 2017 2 02 05 169-180
allfieldsSound	10.1007/s12530-017-9185-9 doi (DE-627)SPR026053128 (SPR)s12530-017-9185-9-e DE-627 ger DE-627 rakwb eng 004 ASE 30.10 bkl 54.72 bkl Borkar, Prashant verfasserin aut Modality of teaching learning based optimization algorithm to reduce the consistency ratio of the pair-wise comparison matrix in analytical hierarchy processing 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract This paper presents an approach to improve the consistency of pair-wise comparison matrix in analytical hierarchy process (AHP) using teaching learning based optimization (TLBO) algorithm. The purpose of this proposed approach to minimize the consistency ratio (CR). Consistency test for the comparison matrix in AHP have been studied rigorously since AHP was introduced in 1970s. However, existing approaches are either too complicated or difficult. Most of them could not preserve the original judgments provided by an expert. To improve the consistency ratio (CR), this research work proposes a simple, effective and efficient method which will minimize the CR to almost zero while preserving the judgment values in pair-wise comparison matrix. The correctness of the proposed method is proved by applying it to two real world case studies reported in literature, namely new product design selection and material selection (work tool combination). The experimentation shows that the proposed approach is efficient and accurate to satisfy the consistency requirements of AHP. Analytical hierarchy process (AHP) (dpeaa)DE-He213 Pair-wise comparison matrix (dpeaa)DE-He213 Teaching learning based optimization (TLBO) (dpeaa)DE-He213 Consistency ratio (dpeaa)DE-He213 Sarode, M. V. verfasserin aut Enthalten in Evolving systems Berlin : Springer, 2010 9(2017), 2 vom: 02. Mai, Seite 169-180 (DE-627)630795533 (DE-600)2561630-4 1868-6486 nnns volume:9 year:2017 number:2 day:02 month:05 pages:169-180 https://dx.doi.org/10.1007/s12530-017-9185-9 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_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_266 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_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 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_2116 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_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_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 30.10 ASE 54.72 ASE AR 9 2017 2 02 05 169-180
language	English
source	Enthalten in Evolving systems 9(2017), 2 vom: 02. Mai, Seite 169-180 volume:9 year:2017 number:2 day:02 month:05 pages:169-180
sourceStr	Enthalten in Evolving systems 9(2017), 2 vom: 02. Mai, Seite 169-180 volume:9 year:2017 number:2 day:02 month:05 pages:169-180
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Analytical hierarchy process (AHP) Pair-wise comparison matrix Teaching learning based optimization (TLBO) Consistency ratio
dewey-raw	004
isfreeaccess_bool	false
container_title	Evolving systems
authorswithroles_txt_mv	Borkar, Prashant @@aut@@ Sarode, M. V. @@aut@@
publishDateDaySort_date	2017-05-02T00:00:00Z
hierarchy_top_id	630795533
dewey-sort	14
id	SPR026053128
language_de	englisch
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author	Borkar, Prashant
spellingShingle	Borkar, Prashant ddc 004 bkl 30.10 bkl 54.72 misc Analytical hierarchy process (AHP) misc Pair-wise comparison matrix misc Teaching learning based optimization (TLBO) misc Consistency ratio Modality of teaching learning based optimization algorithm to reduce the consistency ratio of the pair-wise comparison matrix in analytical hierarchy processing
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topic_title	004 ASE 30.10 bkl 54.72 bkl Modality of teaching learning based optimization algorithm to reduce the consistency ratio of the pair-wise comparison matrix in analytical hierarchy processing Analytical hierarchy process (AHP) (dpeaa)DE-He213 Pair-wise comparison matrix (dpeaa)DE-He213 Teaching learning based optimization (TLBO) (dpeaa)DE-He213 Consistency ratio (dpeaa)DE-He213
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title	Modality of teaching learning based optimization algorithm to reduce the consistency ratio of the pair-wise comparison matrix in analytical hierarchy processing
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title_full	Modality of teaching learning based optimization algorithm to reduce the consistency ratio of the pair-wise comparison matrix in analytical hierarchy processing
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title_sort	modality of teaching learning based optimization algorithm to reduce the consistency ratio of the pair-wise comparison matrix in analytical hierarchy processing
title_auth	Modality of teaching learning based optimization algorithm to reduce the consistency ratio of the pair-wise comparison matrix in analytical hierarchy processing
abstract	Abstract This paper presents an approach to improve the consistency of pair-wise comparison matrix in analytical hierarchy process (AHP) using teaching learning based optimization (TLBO) algorithm. The purpose of this proposed approach to minimize the consistency ratio (CR). Consistency test for the comparison matrix in AHP have been studied rigorously since AHP was introduced in 1970s. However, existing approaches are either too complicated or difficult. Most of them could not preserve the original judgments provided by an expert. To improve the consistency ratio (CR), this research work proposes a simple, effective and efficient method which will minimize the CR to almost zero while preserving the judgment values in pair-wise comparison matrix. The correctness of the proposed method is proved by applying it to two real world case studies reported in literature, namely new product design selection and material selection (work tool combination). The experimentation shows that the proposed approach is efficient and accurate to satisfy the consistency requirements of AHP.
abstractGer	Abstract This paper presents an approach to improve the consistency of pair-wise comparison matrix in analytical hierarchy process (AHP) using teaching learning based optimization (TLBO) algorithm. The purpose of this proposed approach to minimize the consistency ratio (CR). Consistency test for the comparison matrix in AHP have been studied rigorously since AHP was introduced in 1970s. However, existing approaches are either too complicated or difficult. Most of them could not preserve the original judgments provided by an expert. To improve the consistency ratio (CR), this research work proposes a simple, effective and efficient method which will minimize the CR to almost zero while preserving the judgment values in pair-wise comparison matrix. The correctness of the proposed method is proved by applying it to two real world case studies reported in literature, namely new product design selection and material selection (work tool combination). The experimentation shows that the proposed approach is efficient and accurate to satisfy the consistency requirements of AHP.
abstract_unstemmed	Abstract This paper presents an approach to improve the consistency of pair-wise comparison matrix in analytical hierarchy process (AHP) using teaching learning based optimization (TLBO) algorithm. The purpose of this proposed approach to minimize the consistency ratio (CR). Consistency test for the comparison matrix in AHP have been studied rigorously since AHP was introduced in 1970s. However, existing approaches are either too complicated or difficult. Most of them could not preserve the original judgments provided by an expert. To improve the consistency ratio (CR), this research work proposes a simple, effective and efficient method which will minimize the CR to almost zero while preserving the judgment values in pair-wise comparison matrix. The correctness of the proposed method is proved by applying it to two real world case studies reported in literature, namely new product design selection and material selection (work tool combination). The experimentation shows that the proposed approach is efficient and accurate to satisfy the consistency requirements of AHP.
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title_short	Modality of teaching learning based optimization algorithm to reduce the consistency ratio of the pair-wise comparison matrix in analytical hierarchy processing
url	https://dx.doi.org/10.1007/s12530-017-9185-9
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author2	Sarode, M. V.
author2Str	Sarode, M. V.
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doi_str	10.1007/s12530-017-9185-9
up_date	2024-07-03T18:36:26.370Z
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Modality of teaching learning based optimization algorithm to reduce the consistency ratio of the pair-wise comparison matrix in analytical hierarchy processing

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